Skip to main content

Fuel consumption optimization in air transport: a review, classification, critique, simple meta-analysis, and future research implications



This paper presents a review, classification schemes, critique, a simple meta-analysis and future research implication of fuel consumption optimization (FCO) literature in the air transport sector. This review is based on 277 articles published in various publication outlets between 1973 and 2014.


A review of 277 articles related to the FCO in air transport was carried out. It provides an academic database of literature between the periods of 1973– 2014 covering 69 journals and proposes a classification scheme to classify the articles. Twelve hundred of articles were identified and reviewed for their direct relevance to the FCO in air transport. Two hundred seventy seven articles were subsequently selected, reviewed and classified. Each of the 277 selected articles was categorized on four FCO dimensions (Aircraft technology & design, aviation operations & infrastructure, socioeconomic & policy measures, and alternate fuels & fuel properties). The articles were further classified into six categories of FCO research methodologies (analytical - conceptual, mathematical, statistical, and empirical- experimental, statistical, and case studies) and optimization techniques (linear programming, mixed integer programming, dynamic programming, gradient based algorithms, simulation modeling, and nature based algorithms). In addition, a simple meta-analysis was also carried out to enhance understanding of the development and evolution of research in the FCO.

Findings and conclusions

This has resulted in the identification of 277 articles from 69 journals by year of publication, journal, and topic area based on the two classification schemes related to FCO research, published between, 1973 to December- 2014. In addition, the study has identified the 4 dimensions and 98 decision variables affecting the fuel consumption. Also, this study has explained the six categories of FCO research methodologies (analytical - conceptual, mathematical, statistical, and empirical-experimental, statistical, and case studies) and optimization techniques (linear programming, mixed integer programming, dynamic programming, gradient based algorithms, simulation modeling, and nature based algorithms). The findings of this study indicate that the analytical-mathematical research methodologies represent the 47 % of FCO research. The results show that there is an increasing trend in research of the FCO. It is observed that the number of published articles between the period 1973 and 2000 is less (90 articles), so we can say that there are 187 articles which appeared in various journals and other publication sources in the area of FCO since 2000. Furthermore there is increased trend in research on FCO from 2000 onward. This is due to the fact that continuously new researchers are commencing their research activities in FCO research. This shows clearly that FCO research is a current research area among many research groups across the world. Lastly, the prices of jet fuel have significantly increased since the 2005. The aviation sector’s fuel efficiency improvements have slowed down since the 1970s–1980s due to the slower pace of technological development in engine and aerodynamic designs and airframe materials.

We conclude that FCO models need to address the composite fuel consumption problem by extending models to include all the dimensions, i.e. aircraft technology & design, aviation operations & infrastructure, socioeconomic & policy measures, and alternative fuels & fuel properties. FCO models typically comprise all the four dimensions and this reality need to be taken into account in global FCO models. In addition, these models should have objectives or constraints to evaluate the aircraft sizes according to market structure, impact of various policy measures on fuel burn, and near term potential alternative fuel options in the global FCO problem. In the models reviewed, we evaluated that, only the few authors considered these factors. The literature identifies 98 decision variables affecting the fuel consumption related to various dimensions in air transport. So we can conclude that this analysis could represent the informational framework for FCO research in air transport.

Future scope

Our analysis provides a roadmap to guide future research and facilitate knowledge accumulation and creation concerning the application of optimization techniques in fuel consumption of air transport. The addressed dimensions & decision variables could be of potential value to future researchers on the aviation fuel consumption optimization research and is also capable of further refinements. In future, for fuel consumption optimization the explored decision variables could be checked for their reliability and validity and a statistically significant model with minimum number of decision variable could be developed. Further, on the basis of this statistical significant model and with the best market requirement for transport aircraft, the researchers can frame the objective function for fuel consumption minimization problem & decide their dependent variables, independent variables, constant, and constraints. Furthermore, this study will also provide the base for fuel conservation, energy efficiency, and emission reduction in the aviation sector.


Air Transport industry acts as a catalyst to the economic and social development of a nation. This industry encompasses all those activities which involve transportation of goods and people, by air. Air transport connects people, countries and cultures across the face of the globe. Additionally, it opens up a market to global players, thereby supporting trade and tourism significantly.

The Air transport industry has contributed significantly to the growth of commerce, communication, trade and tourism globally. In spite of a marked expansion, the air transport industry is faced with major issues like high fuel consumption, fuel prices, air traffic growth, competition, economic crisis, aviation emission, safety, design and operational challenges. In this study, fuel consumption has been considered, to be a major challenge for the air transport industry. Attributable to high oil prices and an escalation of competition, fuel consumption is rapidly becoming a critical aspect of the air transport industry. Widespread improvement in the global economy during the past year has also contributed to the demand of oil, thereby inflating its price. David L. Greene [1] pointed out that in the early 1970s, air transport doubled its energy efficiency and restrained the growth rate of fuel. In spite of this improvement, energy use by commercial air carriers grew at an annual rate of 2 % from 1970 to 1987. Mohammad Mazraati [2] concluded upon continuously increasing fuel consumption and air traffic. According to this study, world aviation oil demand was 1.18 MB/d in 1971, and reached 4.9 MB/d in 2006. The aviation sector accounts for about 5.8 % of total oil consumption worldwide. Aviation fuel consumption today corresponds to between 2 and 3 % of total fossil fuel use worldwide, more than 80 % of which is used by civil aviation [3]. Emma Nygren et al. [4] predicted that traffic will grow 5 % per year to 2026 and fuel demand 3 % per year. According to Schlumberger [5] the demand for jet fuel and aviation gasoline in the air transport sector is projected to reach 14 % of fuel demand in transportation in 2035, compared to 12 % in 2009.

Fuel consumption is one of major direct operating cost parameter in the air transport industry [6, 7]. Air transport fuel remains the most significant and variable component of operating costs and managing this aspect is an increasing challenge for the air transport sector. Airbus [8] predicted that in 2003, fuel represented about 28 % of total operating cost for a typical A320 family operator. But in the near future, it could be more than 45 % of all operating costs of an aircraft. The economy of a country largely depends on fuel prices. Increases in fuel consumption have an influence on the airlines in two ways; direct impact on the operating cost, and declines the demand for air travel and air cargo. According to Majka A. et al. [9] at one time fuel extraction cost and availability had little impact on the evolution of the air transport industry. Furthermore, aircraft fuel burn is proportional to CO2 emission [10, 11]. Therefore, as the fuel consumption increases the aviation emission shall also increase and that is a big environmental concern today. Chang et al. [12] pointed that the higher fuel consumption of aircrafts is one of the major cause of inefficiency of airlines. Therefore, in such a highly competitive environment, in order to reduce the direct operating cost of an aircraft the FCO is essential. In this study, the FCO in air transport means finding a minimum value of fuel consumption function of several variables subject to a set of constraints and improving the energy efficiency of the aircraft system. The researchers, airlines, aircraft manufacturer and regulatory organizations are continuously trying to reduce the air transport fuel consumption along with the economic cost of flying an aircraft. Further, this reduction will also lead to the reduction of the greenhouse gas emission, caused by the air transport. But before implementing a customized model of the FCO in air transport it is essential to systematically organize, classify, and reviews the published literature and also to identify the factors causing the variation in fuel consumption.

The goal of this study was to examine the historical trends published in fuel consumption optimization (FCO) research studies in air transport industry, and to explore the potential fuel consumption reduction areas in future. We cover the literature that relates to transportation, aerospace sciences, energy & fuel, and environmental sciences. It is hoped that the finding of this research study can highlight the importance of the FCO in the air transport and provide an insight into current FCO research for both academics and air transport industry. The content of this paper is organized as follows: first, the research methodology used in the study is described; second, the methods for classifying FCO research is presented; third, a simple meta-analysis of FCO research are proposed, and the results of the classification are reported; and finally, the conclusions, future research implications, and limitations of the study are discussed.

Research methodology

As the nature of research in the FCO in air transport is difficult to confine to specific disciplines, the relevant materials are scattered across various journals. A number of journals have very few articles on FCO to their name, making it difficult to gain credible simplistic inferences regarding the focus of research in a particular direction. Hence the research journals reviewed have been grouped discipline wise, i.e. Transportation (TP), Aerospace Sciences (AS), Fuel & Energy (F&E), and Environmental Science (ES); all of them being relevant to FCO research.

This gave us some broad fields of foray into the study of the FCO in aviation, letting us draw inferences on the trends in research and research output density in these particular fields. The studies that were selected for inclusion in this study were identified from online electronic databases since from 1973 to 2014. A computerized search of the literature was conducted utilizing Science Direct, Springer Link, Emerald Insight, Jstor, Taylor & Francis, AIAA Journal, SAE Journals, and Google Scholar. Keywords for the computerized search of the literature were: “air transportation fuel consumption optimization”, “fuel efficiency in aviation”, “airline fuel conservation”, “aviation fuel alternatives”, “energy efficiency in aviation”, “aviation emission mitigation” and aviation or jet fuel consumption, which identified approximately 1200 articles. After that the full text of each article was reviewed, to eliminate those that were not actually related to FCO research in air transport. The selection process was mainly based on three criteria as follow: (1) only those articles which clearly described how the mentioned FCO techniques and strategies could be applied were selected. (2) Only those articles that had been published in transportation, aerospace sciences, energy & fuel, and environmental sciences related journals were selected, as these were the most appropriate outlet for FCO research in air transport. (3) Only the papers selected and published in the international journals were included in the study as these journals represents the highest level of research. Unpublished, working papers, conference papers, master and doctoral dissertations and text books were excluded from the study. Based on these criteria we trimmed it down to 277 articles.

Thereafter, each article was carefully reviewed and separately classified according to the four categories of FCO dimensions and seven categories of research methodologies of the FCO in air transport. Though our research may not be exhaustive, it is sufficiently representative for an understanding of FCO research. In addition, this study may suggest/bring light to some unexplored research problems in the area of air transport fuel consumption. The purpose of this paper is mainly descriptive and analytical, thereby not introducing much statistical methodology. Instead, we have conducted a simple meta-analysis to identify trends and patterns in research, in order to shed greater understanding of the development and evolution of research in fuel consumption in the air transport industry and to identify the potential research areas for further research and improvement. We present this simple meta-analysis result in the form of tables and graphs.

Classification method of FCO research in air transport

Classification scheme based on dimensions of FCO in air transport

Based on the literature review carried out and the nature of FCO research observed in air transport, we have introduced a classification scheme to systematically organize the published articles. From the literature survey of articles we have identified five dimensions (1) Aircraft technology & design (2) Aviation operations & infrastructure (3) Socioeconomic & policy measures (4) Aviation alternate fuels, affecting the fuel consumption in air transport. Figure 1 shows the Classification scheme based on the dimensions of FCO research in air transport. They were further classified from the four major dimensions into their respective decision variables. Hileman et al. [13] suggested the advance aircraft design, operational improvements, and alternative fuels for aviation emission reductions. The result of the study showed that the narrower body aircraft has the greatest potential for fuel burn reduction, but it would require the promotion of innovative aircraft design and extensive use of alternative fuels. Grote et al. [14] addressed the technological, operational, and policy measures for fuel burn reduction in civil aviation and the analysis of the study showed that some of the measures were directly implemented on the market because they directly reduce the fuel consumption and fuel cost, but some were not due to market constraints.

Fig. 1

Classification scheme based on dimensions of FCO

Sgouridis et al. [15] examined and evaluated the impact of the five policies for reducing emission of commercial aviation; technological efficiency improvement, operational efficiency improvement, use of alternative fuels, demand shift, and carbon pricing. Similarly the study of Lee & Mo [16]; Green [11]; Lee [3]; Janic [17] and Singh & Sharma [18] collectively identified the above mentioned dimensions of the FCO.

Aircraft technology & design

Today airlines operate in an increasingly competitive environment caused by the globalization of air transport network worldwide and therefore a necessary condition for airlines are commercially successful is the reduction of direct operating costs, which mainly depends on the technological & design characteristics of the aircraft used. Technology development is going on at a rapid rate and we can effectively make use of this technological revolution to reduce the fuel consumption of a commercial aircraft. Moreover the fuel consumption of air transport can be reduced through the variety of options such as increased aircraft efficiency, improved operations, use of alternate fuels, socioeconomic measures, and improved infrastructure, but most of the gain so far have been resulted from the aircraft technological improvement. Aircraft technological improvement mainly depends upon the three factors: structural weight, aircraft aerodynamics, and engine specific fuel efficiency [14]. Moreover the aircraft technological efficiency is described by three aircraft performance metrics: engine efficiencies are expressed in terms of thrust specific fuel consumption (TSFC), aerodynamic efficiencies are measured in terms of maximum lift over drag ratio (Lmax/D) and structural efficiency is quantified using operating empty weight (OEW) divided by maximum takeoff weight (MTOW) [19, 20]. Further, Graham et al. [21] have considered the classical range equation in order to understand how the aircraft technology affects the fuel burn. Fuel consumption per payload range of idealized cruise, keeping the aircraft operating parameters fixed are expressed in terms of aerodynamic efficiency, structural efficiency, engine efficiency, and calorific value of the fuel.

In addition the studies of Henderson et al. [10] and Wang et al. [22] explained the fuel burn reduction by considering aircraft technology & design dimensions. Henderson et al. [10] studied the aircraft design for optimal environmental performance and the design variables considered in this study for optimization problems were from aircraft geometry, engine parameters, and cruise setting. This concludes that the aircraft optimized for minimum fuel burn encompass a high aspect ratio wing with lower induced drag, high bypass ratio engines and high core pressures and temperatures. In addition the mission range and cruise Mach number were also optimized for maximum payload fuel efficiency. Furthermore the possibility of designing larger aircraft for shorter ranges was also examined and result shown that the reduction in structural weight can be achieved by reducing fuel burn. Also, Wang et al. [22] studied the multi objective optimization of aircraft design for emission and cost reduction. A multi-objective optimization of aircraft design for the tradeoff between emission effect and direct operating cost was performed with five geometry variables (i.e. Wing area, aspect ratio, ratio of thickness to chord at root, sweep, and taper ratio), one is mass of the designed fuel for specific range 5000 Km, two flight condition parameters (i.e. cruise Mach number and initial cruise altitude) and three performance requirement as constraints (i.e. take off field length, landing field length, and the 2nd climb gradient). The result of the study showed that, a decrease of 29.8 % in direct operating cost was attained at the expense of an increase of 10.8 % in greenhouse gases. Currently the evolutionary developments of engine technology, airframe technology, and use of advance light weight alloys and composite material, have resulted in a positive trend of fuel efficiency improvements. The merging technology and optimized design dimensions finally lead to the fuel consumption optimization. Aircraft technology & design have the highest potential to optimize the aviation fuel consumption, and some of their successful applications in the FCO have been proposed in the literature [1, 3, 10, 11, 13107].

Aviation operations & infrastructure

The amount of fuel consumed by an aircraft during its operation from start-up through to taxi and takeoff, to cruise, to approach for landing and taxiing on arrival, depends upon several factors. Many of the factors can be influenced by airlines with proper operations planning and strategies. The current operational practices are not always optimal from the fuel consumption point of view and hence there is need for operational improvements. Operational improvement can be expressed in term of operational efficiency, which is the combination of ground and airborne efficiency. In general the actual aircraft performance can be determined by how the aircraft is operated subject to operational constraints and the efficient operational procedures are those, in which the actual fuel burn used falls close to the theoretical minimum [14]. Furthermore the operational efficiency can be expressed in term of operational and payload-fuel energy intensity, and the payload factor [13]. Also the operational factors to reduce the fuel consumption per passenger-km include the increasing load factor, optimizing the aircraft speed and fuel weight, limiting the use of auxiliary power, eliminating the non essential weight, and reducing taxiing. In addition, highly sophisticated flight-planning system also improves the aircraft fuel efficiency because this allows pilots to exploit prevailing wind conditions, calculate precise fuel loads & set different flight levels and speeds for the aircraft to achieve the most economic performance. For a typical flight there are a number of factors such as cruise altitude and speed, mass, and weather conditions that affects the fuel consumption [108]. Therefore, by optimizing the aircraft operations from start-up through to taxi and takeoff, to cruise, to approach for landing and taxiing on arrival, have the significant to reduce the fuel burn.

Aviation infrastructure also plays an important role in fuel consumption optimization. Infrastructure improvements present a major opportunity for fuel consumption reduction in aviation. The design of an airport, including the location of the runways and taxiways relative to terminal buildings, clearly has an effect on aircraft fuel burn, because reduction of delays and decreased taxiing time can provide significant aircraft fuel burn reduction. Airport congestion and improper air traffic management increase the fuel consumption. Airport congestion occurs whenever the actual traffic demand is greater than what the system can handle without the delay. According to Simaiakis et al. [109] airport surface congestion at major airports in the United States and Europe is responsible for increased taxi-out times, fuel burn and emissions. Air Traffic Management (ATM) plays an important role in reducing the environmental impacts of air transportation by reducing the inefficiencies during the operations of an aircraft [110]. Ryerson et al. [111] analyzed the possible fuel savings from Air Traffic Management (ATM) improvements and the study explored the impact of the airborne delay, departure delay, and excess planned flight time, and terminal efficiency in fuel consumption using econometric techniques. In addition the better terminal design can also reduce the fuel consumption. There are a number of ways that airports, airlines and ATM providers can improve the air transportation system to minimize fuel burn and emissions. These include improving the use of the airspace, air traffic control and operations and further improving the use of airspace and air traffic control includes the flexible use of airspace, route redesign, using the new tools and programmes to find most effective route, and reduced separation between the aircraft. Salah [112] developed the model of optimal flight paths taking into consideration jet noise, fuel consumption, constraints and extreme operational limits of the aircraft on approach. The results of this study showed that, the environmental impacts and fuel consumption are reduced by the use of aircraft trajectory optimization during arrivals. Beside this there are some constraints to the improved ATM which includes the air traffic controller (ATC). ATC prevents the ideal trajectory of the aircraft to be flown due to a number of reasons such as safe separation, congested airspace, restricted airspace, delay management and weather avoidance etc. The priorities of controller are also taken into the account. For air traffic controller the safety comes first thereafter the performance. Therefore, by optimizing the aviation infrastructure, there is the potential to reduce fuel consumption. A comprehensive list of the reviewed studies of aviation operations & infrastructure affecting FCO is presented in the literature [1, 3, 7, 1120, 32, 33, 38, 4042, 44, 48, 53, 54, 61, 67, 69, 70, 7275, 85, 86, 94, 95, 98, 104182].

Socioeconomic & policy measures

Aviation is the fastest growing sector of the economy. It provides the number of socioeconomic benefits. There are many socioeconomic & political factors which affect the airline fuel consumption optimization. If these factors are carefully managed then a significant amount of fuel can be saved. Also the social awareness levels of the society, regarding the impact of the aviation emission on climate change plays a key role in fuel consumption reduction. According to Lee & Mo [16] currently, the scientific knowledge and the social demand for low-emission aircraft is not strong enough because the general public is not well aware of the harmful impacts of aviation emissions on the global climate. The strong social pressure sends the signal to the government and the government takes the necessary action after scientifically confirming the problem. As in the cases of the automobile emission and aircraft noise significant technological and operational improvements have been reported, because the general public was well aware of the health damages caused by these [3]. Also, the education and awareness are very important social measure in air transport and there will be many airline customers who have never thought of aviation emission as an environmental problem. Information should be widely available regarding the impact of flying, so that airlines have the background information they need to understand the changing circumstances of aviation. Informed choice is a key component of the transport demand and environmental policy implication. Furthermore, the economic/policy measures for reducing the fuel consumption includes the emission trading, taxes on aviation fuel, and carbon emission charges [17]. Beside this there are some constraints on the airline operations, training, maintenance & reservations, planning & routes, scheduling, airways, and labour, these constraints should be removed for fuel burn reduction [183]. In addition, the economic and policy measures should be introduced in an incremental fashion to give the air transport and consumers time to adjust to the changes. So therefore, by optimizing the socioeconomic & political factors, we can improve the air transportations fuel efficiency. Studies related to socioeconomic & policy measures have been proposed in the literature [2, 3, 1420, 29, 33, 41, 42, 44, 53, 54, 73, 74, 94, 113, 114, 150, 154, 158160, 172, 183224].

Aviation alternative fuels & fuel properties

Aviation alternative fuels can also play an important for the optimization of aviation fuel consumption. Since the energy crises of the 1970s, all the aircraft companies, aviation sectors, engine companies, and other government organization are working for practicality of using alternative fuel in aircraft. A viable alternative aviation fuel can stabilize fuel price fluctuation and reduce the reliance from the crude oil. According to Hileman & Statton [225] economic sustainability, environmental concerns, energy supply diversity, and competition for energy resources are the main drivers for alternative jet fuels development. The replacement for current alternative fuels need no aircraft modifications and can be used with the current aviation system, encompassing existing distribution and refueling infrastructure [226]. Hileman & Statton examined the criteria for the potential alternative jet fuels and highlighted that the synthetic liquid alternative fuels were compatible with current aircraft fleet, but the economic cost of production and the current lack of feedstock availability limits their near term availability to air transport. In addition the study explored the potential of the alternative aviation fuels: conventional jet fuel from petroleum resources, synthetic jet fuels, biodiesel and bio-kerosene, ethanol and butanol, liquefied natural gas and hydrogen and highlighted the technical feasibility parameters: high energy density, high specific energy, high flash point, low freezing point and vapor pressure, high thermal stability, adequate lubricity, and sufficient aromatic compound content. Janic [17]; Pereira et al. [227], Verstraete [228], and Yılmaz et al. [229] studied the liquid hydrogen as an alternative fuel for air transport and these studied identifies the important parameters affecting the fuel consumption. Chuck & Donnelly [230] tested the compatibility of the potential aviation bio-fuels with the Jet A-1 and viscosities, cloud point temperature, flash points, energy content, effect of fuel burn in the range vs. the payload were studied. The result of the study shown that, only the hydrocarbons, matched the range vs. payload of Jet-A1 and the limonene was found to fulfill the required specification. Therefore a suitable alternative fuel can be selected on the basis of a variety of criteria, societal priorities, economic viability, and sustainability considerations, which will further reduce the aviation fuel consumption. Aviation alternative fuels & fuel properties studies related to FCO have been proposed in the literature [3, 11, 1318, 32, 33, 4042, 53, 54, 59, 79, 86, 94, 104, 140, 150, 194, 225282].

Identifications of decision variables based on FCO dimensions

Further, the decision variables of respective dimensions of FCO were selected from the literature on the basis of the description and examination of the relationships between fuel consumption and respective dimensions & their variables, logical reasoning, conceptual basis, and strong influence on fuel burn. Theses dimensions & their respective decision variables affect the fuel consumption in an air transport indirect way and indirectly. As clearly evident from the literature these dimensions are closely related to each other so care has been taken that, a single decision variable cannot be repeated more than one time under the two different dimensions. Table 1 shows the decision variables based on the identified dimensions and the reviewed literature. Table 2 shows the number of decision variables of respective dimension and their percentage. From Table 2 it is clear that the A had the highest percentage of decision variables (48.99 %), while B dimension has 23.47 %, and C has 13.26 % and D has 14.28 % each.

Table 1 Identified the decision variables based on the FCO dimensions
Table 2 Percentage of identified decision variables of FCO dimensions

Classification scheme based on research methodologies of FCO research

Figure 2 shows the classification scheme 2 based on the research methodology related to fuel consumption & optimization studies in air transport. The fuel consumption & optimization research in air transport on the basis of research methodology could be grouped broadly into two major classifications of analytical and empirical research. Further, they are classified into three subcategories of each major classification, i.e. analytical-conceptual, mathematical, statistical, and empirical-experimental, statistical, and case studies. Furthermore analytical- mathematical techniques include the linear programming, mixed integer programming, dynamic programming, gradient based algorithms, simulation modeling, and nature based algorithms. Analytical research uses the deductive methods while the empirical research uses the induction method to arrive at conclusions. Analytical-research consists the logical, mathematical, and statistical methods [283]. Table 3 shows the research methodologies FCO in air transport.

Fig. 2

Classification scheme based on research methodology on fuel consumption & optimization studies

Table 3 Research methodologies for FCO research in air transport

Analytical research methodology

In this study, the analytical research includes the case studies for conceptualization, intro-respective research, and conceptual modeling for fuel consumption research in air transport [1, 3, 4, 11, 13, 14, 16, 19, 21, 32, 34, 38, 40, 41, 4345, 49, 50, 5254, 57, 59, 61, 73, 7983, 87, 92, 94, 96, 97, 100, 101, 103, 105, 106, 110, 128, 140, 146, 148, 151, 155, 163, 164, 168, 174, 175, 183, 187, 193, 201, 209, 211, 213, 215, 218220, 225, 231, 232, 236, 258, 267, 273]. Analytical mathematical research develops the new mathematical relationships between closely defined concepts and uses the simulated data to draw the conclusions [284, 285]. Here the analytical-mathematical research for fuel consumption in aviation includes the; fuel burn and emission prediction and forecast for future scenario studies which primarily consist of logical and descriptive modeling [2, 22, 25, 29, 35, 37, 42, 4648, 55, 6365, 67, 69, 70, 72, 7578, 84, 85, 8891, 93, 95, 98, 99, 102, 104, 107, 113115, 117, 132, 133, 136, 138, 147, 149, 152, 153, 165, 178182, 184, 188, 190, 191, 199, 212, 244]. Additionally the analytical-mathematical techniques can further be classified into the linear programming [24, 28, 39, 62, 108, 116, 122125, 129, 134, 150, 157, 160, 162, 166, 170, 177, 185, 192, 197, 198, 203, 216, 217, 221, 224], mixed integer programming [12, 135, 197, 221, 224], dynamic programming [17, 75, 76, 107, 119, 154, 159, 161, 171, 186, 189], gradient based methods [26, 27, 30, 31, 36, 51, 56, 60, 71], simulation modeling [15, 112, 121, 142, 227, 228], and nature based algorithms [10, 58, 66, 68, 118, 120, 176]. These techniques mainly deal with the FCO models that are the main thematic area of this study. Each of these techniques has its own strengths and weaknesses and can be helpful in solving certain types of FCO problems. Mathematical programming models have been demonstrated to be useful analytical tools in optimizing decision-making problems such as those encountered in air transport fuel consumption.

Linear programming (LP) models consist of a linear fuel consumption function which is to be minimized subject to a certain number of constraints [157, 162]. Mixed integer programming (MIP) is applicable when some or all of the variables are restricted to be integers [286]. Dynamic programming is used when sub problems are not independent and we solve the problem by dividing them into sub problem [284]. As the aircraft fuel consumption during its operation is not always linear in nature, therefore complex mathematical relationships are used for the FCO. The mathematical techniques, i.e. linear programming and MIP may not be very effective in solving real world FCO problems, because of the large number of variables and constraints involved. These are only suitable for solving the FCO problems with limited variables and constraints and also LP require high computer memory and long CPU time in order to process complex mathematical algorithms [287]. Linear programming has shown to be incapable of describing the actual complexity of realism of FCO models. Also the dynamic programming has the limitations: lack of general algorithms and dimensionality [284].

Gradient based methods are mainly used for aerodynamic design optimization of aircraft and they minimize the convex differential functions. Gradient-based methods provide a clear convergence criterion. The limitations of gradient-based methods are; high development cost, noisy objective function spaces, inaccurate gradients, categorical variables, and topology optimization [285]. This limits their use for global FCO. Simulation modeling in the area of the FCO is used to observe how an aircraft performs, diagnose problems and predict the effect of changes in the aircraft system, evaluates fuel consumption, and suggest possible solutions for improvements. Simulation techniques can be ideal for reproducing the behaviors of a complex design system of the aircraft. Many previous studies have analyzed the capability of simulation modeling in fuel consumption modeling and optimization [15, 112, 121, 142, 227, 228]. One of the major limitations of simulation techniques is its inability to guarantee optimality of the developed solution. Also the simulation technique is very expansive.

The nature based algorithms can be based on swarm intelligence, biological systems, physical and chemical systems [288]. The researchers have learned from biological systems, physical and chemical systems to design and develop a number of different kinds of optimization algorithms that have been widely used in both theoretical study and practical applications. Since the nature is the main source of inspiration of these algorithms, so they are called nature based algorithms [288, 289]. In FCO problems the nature based algorithms are classified into the genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing, and immune algorithm. GA is an evolutionary based stochastic optimization algorithm with general-purpose search methods which simulate the processes in a natural evolution system [290]. GA is an efficient algorithm with flexibility to search the complex spaces such as the solution space for the global air transport fuel consumption. GA algorithms are well suited to multi-objective optimization problems because they can handle large populations of solutions [58]. The advantages of using GA techniques for solving large optimization problems are its ability to solve multidimensional, non-differential, non-continuous, and even nonparametric problems [291]. Moreover, it solves the problem with multi solutions. GAs has been proven to be a highly effective and efficient tool in solving complex aircraft design, and some of their successful applications in the optimization of fuel consumption models have been proposed in the literature [58, 68, 176]. There are, however, a number of challenges when designing a customized GA procedure to solve a certain FCO problem. The first difficulty is the construction of customized genetic operators to perform the mating process on the chromosomes. Secondly, designing a constraint handling mechanism is generally a complicated task in order to ensure the effective implementation of the model constraints. In addition, when populations have a lot of subjects, there is no absolute assurance that a genetic algorithm will find a global optimum [290]. PSO has been extensively used to many engineering optimization areas due to its simple conceptual framework, unique searching mechanism, computational efficiency, and easy implementation [290]. In order to find the optimal solution, the PSO algorithm simulates the movement of a set of particles in the search space under predetermined rules [292]. The particles use the experience accumulated during the evolution, for finding the global maximum or minimum of a function [118]. The PSO algorithm does not require sorting of fitness values of solutions in any process and this might be a significant computational advantage over GA, especially when the population size is large [293].

Simulated annealing (SA) is a one of the most common meta-heuristics techniques, and has been successfully applied to solve several types of combinational optimization problems [294]. The main advantages of SA are; it deals with arbitrary systems and cost functions, relatively easy to code, even for complex problems. But its main disadvantage is that, it cannot tell whether it has found an optimal solution, it requires some complimentary bound [295]. Pant, R. [66] used SA for the aircraft configuration and flight profile optimization. In case of aircraft fuel consumption, the objective function was found to be highly nonlinear and discontinuous, with several combinations of design variables not having a feasible solution. Hence, gradient-based optimization methods could not be applied to obtain the optimal solution, and the SA approach was adopted [66]. Ravizza, S. et al., [120] adopted the population based immune algorithm for tradeoff between the taxi time and fuel consumption in airport ground movement. Immune Algorithms are related to the Artificial Immune Systems field of study concerned with computational methods. Immune Algorithms are inspired by the process and mechanisms of the biological immune system. The main advantages of the algorithm are dynamically adjustable population size, combination of local with global search, defined convergence criterion, and the capability of maintaining stable local optimum solutions [296]. More knowledge about the fuel-based objective function is needed to formulate the combined FCO function. Lastly the analytical-statistical research integrates logical, mathematical models from analytical-research and statistical models from empirical research for fuel consumption & optimization research. Table 2 shows the list of analytical statistical studies [20, 23, 109, 111, 130, 137, 144, 145, 156, 158]. Summarily, the main objective of analytical statistical research is to provide, the more cohesive model for empirical statistical testing [283].

Empirical research methodology

The empirical research methodology uses data from external organizations or businesses to test if relationships hold in the external world [283]. Empirical research methods for fuel consumption & optimization studies are classified into three sub-categories, namely; empirical-experimental [86, 204, 229, 230, 233235, 238240, 243, 245247, 250252, 256, 259, 261, 263265, 268271, 281, 282], empirical statistical [18, 126], and empirical case studies [131, 138, 141, 167, 169, 172, 187, 194, 195, 205208, 210, 214, 222, 223, 226, 237, 241, 242, 248, 253255, 257, 260, 262, 266, 272, 274280]. The empirical-experimental research examines the relationships by manipulating controlled treatments to determine the exact effect on specific dependent variables [283, 297]. The empirical- experimental research methodology for fuel consumption & optimization studies are mainly consist of fuel properties and optimization studies. The main advantage of using the empirical-experimental research is, it may understand and respond more appropriately to dynamics of situations of fuel consumption. The main purpose of empirical statistical research methodologies is to empirically verify theoretical relationships in larger populations from actual practices for reducing the number of relationships for future application [283, 297]. Literature reports the two empirical statistical analyses [18, 126], in which fuel consumption models are tested for their reliability and validity. Lastly, the empirical case study examines the organizations across time and provides the dynamic dimension to theory for promoting the theoretical concepts [283]. Moreover, the empirical case studies provide new conceptual insights by empirically investigating individual cases of complex fuel consumption relations of the real world.

A simple meta-analysis

In general, the nature of data available in the studies reviewed determines the type of meta-analytic method that can be applied. In this paper, we perform summary counts of the determinants of the article studied, fuel prices, and evolution of fuel efficiency trends. Though this simple meta-analysis provides only descriptive information with no statistics, it is expected to shed greater understanding of the development and evolution of FCO research trends in the air transport industry and to identify potential research areas for further research and for improvement. Accordingly, we analyzed 277 articles related to FCO research in air transport by (1) Yearly distribution of articles, and evolution of fuel prices and fuel efficiency trends (2) Distribution of research methodologies (3) Journal wise (Discipline) distribution.

Yearly distribution of research articles, fuel prices and evolution of fuel efficiency trends

Progresses in literature related to fuel consumption have been started since after 1973–74 Arab oil embargoes. After that, the oil crises fuel conservation and efficiency became the main focus of the aviation industry. Table 4 Yearly distributions of research articles, fuel prices, and evolution of fuel efficiency trends of air transport from 1973 to 2014 [298]. The major growth in optimum use of fuel occurred after the 1973 Arab oil embargo. During the period 1973–1980, the oil prices increased sharply and U.S. economy had focused the need for more fuel efficient transportation [98]. The first oil shock was in 1973–1974 and the second one in 1978–1980 [16]. During the period 1973–1975, the oil prices increased sharply as shown in Table 4, while the airline jet fuel prices stabilized in 1976 compared to sharply rising prices in the three preceding years. The jet fuel price in 1975 rose to about 2.01 dollar/ million BTU, from the 1.54 dollar/ million BTU in 1974. During the period 1973–1975, the net average percentage change in fuel prices was 51 % and during the period 1976–1978, the fuel prices increased by an amount 8-13 %, this shows the stability of jet fuel prices. But, again during the period (1978–1980) second oil shock the jet fuel prices increased sharply, by net average percentage 49 % and this was only 2 % less than the 1973–1975 time periods. Also increased air travel volume was one more main reason behind the rising fuel prices, because the passengers were relatively unconcerned to the ticket price because the benefits of faster travel and this was a very interesting trend in that period [16]. Table 4 shows the distribution of research articles during the period 1973–1980. Total number of articles from 1973 to 1980 were 38 and most of the studies have been found in 1978 i.e.9. It is clear from the Table 4, that the numbers of the articles during the first oil shock (1973–1975) were 9 and after first oil shock and second oil shock, they have been increased to 29. Figure 3 shows the yearly distribution of a number of articles and fuel prices.

Table 4 Yearly distributions of research articles, fuel prices, and evolution of fuel efficiency trends [298]
Table 5 Percentage (%) of research methodologies for FCO research
Fig. 3

Yearly distribution of number of articles and jet fuel prices

In the early 1980s, the non OPEC countries had also started production of oil therefore oil consuming counties decreased their oil demand from OPEC countries. As a result the OPEC production declined after 1981 and in response to declining production. Furthermore, Iran and Iraq war, and ceasing of oil production by Saudi Arabia were the main reasons for fuel price decline [299]. During the period 1981–1985 the US airline jet fuel prices declined from 7.49 to 6.51 dollar/million BTU and also the net average % decline in fuel prices was 3.4 %. But, the biggest decline in jet fuel prices occurred in 1986, during this year the jet fuel prices decreased by 32 % as compared to 1985 prices. After, the 1986 to 1989 the fuel prices stabilized with net average % change of only 2 %. Again, in 1989 the fuel prices increased by 28 % as compared to 1988 prices. The 1990 spike was mainly attributable to the first Gulf War, but the price spike was only for shorter periods [299]. It is clear from the Fig. 3 that the fuel prices from 1973 to 1981 increased continuously and from 1981 to 1989 decreased continuously. The total numbers of articles during this period were 23. Most of the studies have been found in 1987 i.e. 8. During the period 1981–1990, the number of articles also decreased as compared to 1973–1980.

In the period 1991–2003 the jet fuel prices remained relatively low and stable. During the period 1991–1995, the fuel prices continuously decreased and they fell from 5.18 to 4.04 dollar/million BTU. In 1998 oil prices were affected by the Asian financial crisis. They fell to below 25 % as that of 1997 jet fuel prices. But, the Asian economies recovering from the financial crisis, prices increased during 2000. The fuel prices rose by 63 % as compared to that of 1999 prices. The total number of articles from 1991 to 2003 were 61 and most of the studies have been found in 2003 i.e.9. The numbers of the articles were more than the last two decades.

During 2004–2014 world aviation fuel consumption and its production increased to a greater extent. The rising demands of countries such as China and India, and political instability in Venezuela, Nigeria, Russia and particularly Middle East have troubled oil supplies and raising prices [300]. From Fig. 3 it is clear that the fuel prices rose sharply from 2002 to 2008 and during the period 2004–2009, the fuel prices experienced large fluctuations from 2004 to 2009. In 2008 jet fuel prices reached levels more than three times those of 2003. While in 2009 fuel prices fell from their 2008 high, and it all most reached half of 2008 fuel prices. This spike and decline in jet fuel prices have demonstrated uncertainty in the magnitude of future fuel prices. Again, in 2011 the jet fuel prices rose by 6.34 dollar/million BTU more than those of 2010 and after that from 2012 to 2014 they decline net average % of 6.20. During the period 2004–2014 the numbers of research studies have also been increased. Table 4 shows, the total numbers of articles from 2004 to 2014 were 165, which is more than the number of articles than from 1973 to 2003. Most of the studies have been found in 2014 i.e. 28. Figure 3 shows the increasing trend of number of articles from 2004 to 2007 and during the same period the oil prices had also increased. But in the last 2 years the total numbers of articles were 50, and this represents the 18 % of the total number of articles. From Table 4 it is observed that the number of published articles between the period 1973 and 2000 is less (90 articles), than that of the period 2001–2014.

Historically, jet fuel prices have been the main driver for improvements in aircraft fuel efficiency [16]. Table 4 also shows the yearly evolution of the fuel efficiency trends from 1973 to 2014. Evolution of fuel efficiency trends has explored the four factors .i.e. technological efficiency improvement, operational efficiency improvement, socioeconomic & policy measures, and alternate fuel use, similar as that identified earlier. But, the alternative fuels have only shown the future potential options for fuel efficiency improvement, because of concerns regarding their economic cost of production and the current lack of feedstock availability limits their near term availability of aviation [225]. So, only the improvement in aircraft fuel efficiency from 1960 to 2014 were mainly due to technological factors, operational factors, load factors, and aircraft size. The various trends evolved in the Table 4 are also grouped under these four factors. From Table 4 it is clear that the entire fuel efficiency factor have been evolved continuously from 2007 to 2014, as compared to other time span. According to Grote, [14] average fuel-efficiency improvement between 1960 and 2008 was 1.5 % per annum, but over the time it has slowed down. Lee et al. [20] predicted that the reduction in energy intensity during the period 1959–1995 were mainly due to improvement in engine efficiency (57 %), aerodynamic efficiency (22 %), aircraft capacity (17 %), and other changes such as increased aircraft size (4 %). Owen, [301] showed a 70 % improvement in fuel efficiency as fuel per RPK between 1970 and 2006 and these improvements were mainly due to improvements in load factor (20 %), aircraft size (26 %) and finally technical and operational improvements to the fleet (24 %). However a great part of this improvement was gained during the 1970–1980 (40 %) and rest of improvements had been achieved during 1980–1990 (22 %), 1990–2000 (23 %), and 2000–2006 (15 %). Figure 4 shows the evolution of fuel efficiency trends in US domestic and international aviation from 1970 to 2013. It is clear from Fig. 4 that the US domestic and international airlines passengers’ air traffic, fuel consumption decreased from 9 liters/100Km to 3 liters/100Km and 10 liter/100Km to 4 liters/100Km during the time period from 1970 to 2013. Improvements were particularly rapidly during the 1970s, when wide body aircraft came into the service and in the early to mid-1980s, when mid-range aircraft like turbofan 2nd and third generation entered into the service. Figure 4 shows the 60 % and 52 % reduction of fuel burn of US domestic and international airlines on a seat-Km (passengers only) during the time period 1970–1985. The flattening slope of the fuel burns curve in Fig. 4 suggests a notable decrease in the rate of fuel efficiency improvement over the time period 1985–2000. Through 1985–2000, we estimate that the efficiency of aircraft improved 15 % for both airlines. Lastly, the figure shows that the fuel efficiency improvement of 9 % and 11 % for the domestic and international US airlines during the time period 2001–2014.

Fig. 4

Evolution of fuel efficiency trends in US domestic and international aviation from 1970 to 2013 [302]

Distribution of articles by research methodologies of FCO research

Table 5 shows the percentage distribution of research methodologies for the FCO research in air transport. It is clear from the Table 5 that the analytical research methodologies have the higher percentage (75 %) than the empirical research methodologies (15 %). Also, the near about haft of the research methodologies are from the analytical- conceptual, logical, and descriptive modeling. These studies mainly include the; fuel burn and emission calculation, prediction and forecast for future scenario. Moreover, the analytical-conceptual, logical, and descriptive modeling, empirical-experimental and, empirical case studies are more predominately proposed by many researchers 72 % of methodologies rather than 21 % methodologies of optimization modeling. It is also observed from Table 5 that the optimization modeling research techniques, i.e. Linear programming, dynamic programming, MIP, gradient based methods, and natural algorithms have very low percentage (21 %).

Distribution of articles by journals (discipline wise)

The journal wise the number of FCO research articles in international journals is computed and the same is shown in Table 6. During the period 1973 to 2014 there are 277 research articles on FCO research appeared in 69 journals and most of the article have been found in Journal of Air Transport Management (9 %) followed by Transportation Research Part D: Transport and Environment, and International journal of Hydrogen Energy both having 7 % each. Since the numbers of articles against many journals are few to get some simple inferences, the research journals reviewed are grouped with respect to disciple wise, i.e. Transportations (TP), Aerospace Sciences (AS), Fuel & Energy (F&E) and Environmental Science (ES). Table 6 shows the discipline wise total % of the articles. Accordingly, distribution of articles of journals (discipline wise) is computed and shown in Fig. 5. It is observed that the Transportation (TP) related journals have far the most articles i.e. 111. This indicates that TP is a major important field affecting the FCO in air transport. Followed to TP related journals, the Aerospace sciences (AS) are having more articles on FCO research. The differences between the TP and AS articles are only 8 %. This could be due to the fact most of the articles reported in TP are also related to road and rail transport and those were not included in the study. The number of articles that appeared in discipline F&E and ES are relatively low with discipline TP, and AS. This could be due to the low correlation between the objectives of various studies reported on FCO research in air transport and scope of the respective journals.

Table 6 The discipline wise total % of articles
Fig. 5

Discipline wise percentage of articles

Findings, conclusions, direction for future research implications, and limitations

It is known that the history of the FCO research is not long compared with other industries. To the best of our knowledge, so far, no attempt has been made to classify and analyze the literature dealing with FCO with air transport research. Thus, in this paper we have attempted to review and classify the FCO research. Accordingly, an extensive literature review has been attempted from various journals and web based articles that are possible outlets for this research. This resulted in the identification of 277 articles from 69 journals by year of publication, journal, and topic area based on the two classification schemes related to FCO research, published between, 1973 to December- 2014. In addition, the study has identified the 4 dimensions and 98 decision variables affecting the fuel consumption. Also, this study have explained the six categories of FCO research methodologies (analytical - conceptual, mathematical, statistical, and empirical-experimental, statistical, and case studies) and optimization techniques (linear programming, mixed integer programming, dynamic programming, gradient based algorithms, simulation modeling, and nature based algorithms). The findings of this study indicate that the analytical-mathematical research methodologies represent the 47 % of FCO research. The results show that there is an increasing trend in research of the FCO. It is observed that the number of published articles between the period 1973 and 2000 is less (90 articles), so we can say that there are 187 articles which appeared in various journals and other publication sources in the area of FCO since 2000. Furthermore there is increased trend in research on FCO from 2000 onward. This is due to the fact that continuously new researchers are commencing their research activities in FCO research. This shows clearly that FCO research is a current research area among many research groups across the world. Lastly, the prices of jet fuel have significantly increased since the 2005. The aviation sector’s fuel efficiency improvements have slowed down since the 1970s–1980s due to the slower pace of technological development in engine and aerodynamic designs and airframe materials.

From the matching of published articles according to our proposed classification schemes and according to performance metric, it seems there are considerable untouched research problems in FCO research. Over the last four decades, the significance of FCO at tactical and operational levels has been recognized by academics and practitioners as a competitive advantage for the better performance of airlines. This study reviewed the state of the art in optimization modeling of fuel consumption. Our findings have some important conclusions of FCO research and suggest the following directions for future research in the area:

  • We classified the current literature into four dimensions based on the degree of complexity and identified 98 decision variables affecting the FCO. This classification of dimensions and their respective decision variables could be of potential value to future researchers in the field and is also capable of further refinements. These parameters, if addressed, could result in a consistent, and comparable database of the FCO research.

  • We conclude that FCO models need to address the composite fuel consumption problem by extending models to include all the dimensions, i.e. aircraft technology & design, aviation operations & infrastructure, socioeconomic & policy measures, and alternative fuels & fuel properties. FCO models typically comprise all the four dimensions and this reality need to be taken into account in global FCO models. In addition, these models should have objectives or constraints to evaluate the aircraft sizes according to market structure, impact of various policy measures on fuel burn, and near term potential alternative fuel options in the global FCO problem. In the models reviewed, we evaluated that, only the few authors considered these factors.

  • We also conclude that the performance measures (.i.e. technological efficiency and operational efficiency) adopted in FCO models need to be broadened in definition to address socioeconomic & political and alternative fuels potentials. Although real FCO models emphasize a variety of performance measures in practice—none of FCO models allow for this variety.

  • A second classification was also presented in the paper based on the research methodologies and techniques used for tackling the proposed fuel consumption problems. One perpetual concern is the development of appropriate research approaches for tackling large fuel consumption and optimization problems. Various research techniques have been used to deal with aircraft design, operations, infrastructure, socioeconomic & political, and alternate fuel problems ranging from mathematical models; gradient based algorithms, simulation modeling, and to the latest nature based algorithms. Hence, there is a need to further extend the effectiveness of the existing solution techniques to be capable of handling realistic FCO problems with large numbers of variables and constraints. Heuristics and meta-heuristic techniques are still the dominant solution techniques in the literature of FCO [10, 58, 66, 68, 118, 120, 176]. Genetic algorithms (GAs), particle swarm optimization (PSO), simulated annealing (SA), and immune algorithm (IA), has been recognized by several researchers as the most promising techniques. There is still a need to further extend the effectiveness of the existing research methodologies and to test the new arrivals such as Ant Colony Optimization (ACO), Bee Colony Optimization (BCO) techniques, and Firefly optimization techniques (FA).

  • Only 1 % and 2 % articles discussed the empirical statistical methodology and analytical-mathematical methodology based on the natural algorithms within the context of the FCO. As far as the empirical statistical research methodology is concerned, it verifies models for their empirical validity in larger populations to reduce the number of relationships in future research, while the nature based algorithms has been considered the most powerful tool for optimization. More research could be done on this issue. Therefore, we observed that, the combination of empirical statistical methodology followed by analytical- mathematical nature based algorithms could be of potential research methodologies to future researchers in the field.

  • It is observed that the number of published articles between the period 1973 and 2000 is less (90 articles), so we can conclude that there are 187 articles which appeared in various journals and other publication sources in the area of FCO since 2000. With this it is possible to comment that on an average 13 articles per year appeared in journals/other publication sources related to FCO research since 2000. Furthermore there is increased trend in research on FCO from 2000 onward. This is due to the fact that continuously new researchers are commencing their research activities in FCO research. This shows clearly that FCO research is a current research area among many research groups across the world.

  • The prices of jet fuel have significantly increased since the 2005. If air transport improves their fuel efficiency in response to increase in jet fuel prices, then some of the increases in the cost of air travel can be reduced. The aviation sector’s fuel efficiency improvements have slowed down since the 1970s-1980s due to the slower pace of technological development in engine and aerodynamic designs and airframe materials. Technological improvements will take a long time for development, while the operational change is most near-term, could lead to significant reduction in air fares in the face of much higher oil price, but it may not achieve a significant option given the fast increase in air travel demand. Also the study has evolved the various trends of aircraft technological factors, and operational factors for fuel efficiency improvement. These factors could be potential options for the FCO.

  • Also an important outcome of the analysis of trends in literature output was that we noticed clear parallels between interest in FCO research and global occurrences related to the oil and energy industry, whether social, political or economic, whether scheduled or sudden; whether positive or degrading to the energy sector. And hence, ultimately, oil prices seem closely related to interest in the FCO.

  • In addition a total of 277 articles were classified according to our classifications. We analyzed the identified articles from the 69 journals by year of publication, journal, and topic area. This particular analysis could provide guidelines for the pursuit of future research on FCO and its applications by explaining the chronological growth of aviation fuel efficiency over the years, the challenging areas of fuel efficiency improvement and application, and the major issues surrounding environmental impact, fuel prices, and competitions among the airlines.

  • Finally, we acknowledge that this review cannot be claimed to be exhaustive, but it does provide a reasonable insight into the state-of-the-art on FCO research. Thus, it is hoped that this review will provide a source of reference for other researchers/readers interested toward FCO research and help stimulate further interest. Future work will concentrate on the development of an appropriate information framework for FCO research in air transport. After that, this informational framework should be checked for reliability and validity. This leads to the development of a structural model of fuel consumption in the air transport industry and further knowing the relationships among the variables an optimization model will be constructed. Furthermore, this study will also provide the base for fuel conservation, energy efficiency, and emission reduction (As CO2 emission are proportional to aircraft fuel burn) in the aviation sector.

This study might have some limitations. Readers should be cautious in interpreting the result of this study, since the findings are based on the data collected only from the international journal articles. The journals articles were mainly from; Transportations (TP), Fuel & Energy (F&E), Aerospace Sciences (AS), and Environmental Sciences (ES). Only, the 69 journal of these disciplines were included in the study. There might be other academic journal which may be able to provide a more comprehensive picture of the articles related to the application of the FCO in air transport. Second, we have reviewed academic/professional journals articles only; conference proceedings and dissertation were excluded, as we assumed that high quality research eventually published in academic/professional journals. Lastly, non-English publications were excluded from this study. We believe research regarding the application of FCO techniques have also been discussed and published in other languages.


  1. 1.

    Greene DL (1992) Energy-efficiency improvement potential of commercial aircraft. Annu Rev Energy Environ 17:537–573

    Article  Google Scholar 

  2. 2.

    Mazraati M (2010) World aviation fuel demand outlook. OPEC Energy Rev 34:42–72

    Article  Google Scholar 

  3. 3.

    Lee JJ (2010) Can we accelerate the improvement of energy efficiency in aircraft systems? Energy Conserv Manag 51:189–196

    Article  Google Scholar 

  4. 4.

    Nygren E, Aleklett K, Höök M (2009) Aviation fuel and future oil production scenarios. Energy Policy 37(10):4003–4010

    Article  Google Scholar 

  5. 5.

    Schlumberger CE, Wang D (2012) Air transport and energy efficiency. The International Bank for Reconstruction and Development / The World Bank, Transport papers TP-38

  6. 6.

    Airbus (2004) Getting to grip with fuel economy. Flight Oper Support Serv 4

  7. 7.

    Stolzer AJ (2002) Fuel consumption modeling of a transport category aircraft using flight operations quality assurance data: a literature review. J Air Transp 7(1):93–102

    Google Scholar 

  8. 8.

    Airbus (2008). Getting to grip with A320 family performance retention and fuel savings. Flight Oper Support Serv 2

  9. 9.

    Majka A, Brusow V, Klepack Z (2007) Fuel Consumption and transportation energy effective analysis. Eur Personal Air Transp Syst Stud, EP- D4.3, SFC-V0, 1–23.

  10. 10.

    Henderson RP, Martins JRRA, Perez RE (2012) Aircraft conceptual design for optimal environmental performance. Aeronaut J 116(1175):1

    Article  Google Scholar 

  11. 11.

    Green JE (2009) The potential for reducing the impact of aviation on climate. Tech Anal Strat Manag 21(1):39–59

    Article  Google Scholar 

  12. 12.

    Chang YT, Park HS, Jeong JB, Lee JW (2014) Evaluating economic and environmental efficiency of global airlines: a SBM-DEA approach. Transp Res Part D: Transp Environ 27:46–50

    Article  Google Scholar 

  13. 13.

    Hileman JI, De la Rosa Blanco E, Bonnefoy PA, Carter NA (2013) The carbon dioxide challenge facing aviation. Prog Aerosp Sci 63:84–95

    Article  Google Scholar 

  14. 14.

    Grote M, Williams I, Preston J (2014) Direct carbon dioxide emissions from civil aircraft. Atmos Environ 95:214–224

    Article  Google Scholar 

  15. 15.

    Sgouridis S, Bonnefoy PA, Hansman RJ (2011) Air transportation in a carbon constrained world: long-term dynamics of policies and strategies for mitigating the carbon footprint of commercial aviation. Transp Res A Policy Pract 45(10):1077–1091

    Article  Google Scholar 

  16. 16.

    Lee J, Mo J (2011) Analysis of technological innovation and environmental performance improvement in aviation sector. Int J Environ Res Public Health 8(9):3777–3795

    Article  Google Scholar 

  17. 17.

    Janić M (2014) Greening commercial air transportation by using liquid hydrogen (LH2) as a fuel. Int J Hydrog Energy 39(29):16426–16441

    Article  Google Scholar 

  18. 18.

    Singh V, Sharma SK (2014) Evolving base for the fuel consumption optimization in Indian air transport: application of structural equation modeling. Eur Transp Res Rev 6(3):315–332

    Article  Google Scholar 

  19. 19.

    Babikian R, Lukachko SP, Waitz IA (2002) The historical fuel efficiency characteristics of regional aircraft from technological, operational, and cost perspectives. J Air Transp Manag 8(6):389–400

    Article  Google Scholar 

  20. 20.

    Lee JJ, Lukachko SP, Waitz IA, Schäfer A (2001) Historical and future trends in aircraft performance, cost and emission. Annu Rev Energy Environ 26:167–200

    Article  Google Scholar 

  21. 21.

    Graham W R, Hall C A, Vera Morales M (2014) The potential of future aircraft technology for noise and pollutant emissions reduction. Transp Policy

  22. 22.

    Wang Y, Yin H, Zhang S, Yu X (2014) Multi-objective optimization of aircraft design for emission and cost reductions. Chin J Aeronaut 27(1):52–58

    Article  Google Scholar 

  23. 23.

    Chang R C (2014) Examination of excessive fuel consumption for transport jet aircraft based on fuzzy-logic models of flight data. Fuzzy Sets Syst

  24. 24.

    Cusher AA, Gopalarathnam A (2014) Drag reduction on aircraft configurations with adaptive lifting surfaces. Aerosp Sci Technol 34:35–44

    Article  Google Scholar 

  25. 25.

    Dray L (2014) Time constant in aviation infrastructure. Transp Policy 34:29–35

    Article  Google Scholar 

  26. 26.

    Della Vecchia P, Nicolosi F (2014) Aerodynamic guidelines in the design and optimization of new regional turboprop aircraft. Aerosp Sci Technol 38:88–104

    Article  Google Scholar 

  27. 27.

    Liem R P, Kenway G K, & Martins J R (2014) Multi-mission aircraft fuel-burn minimization via multipoint aero-structural optimization. AIAA J 1–19

  28. 28.

    Tsai WH, Chang YC, Lin SJ, Chen HC, Chu PY (2014) A green approach to the weight reduction of aircraft cabins. J Air Transp Manag 40:65–77

    Article  Google Scholar 

  29. 29.

    Dray L (2013) An analysis of the impact of aircraft lifecycles on aviation emissions mitigation policies. J Air Transp Manag 28:62–69

    Article  Google Scholar 

  30. 30.

    Mastroddi F, Gemma S (2013) Analysis of Pareto frontiers for multidisciplinary design optimization of aircraft. Aerosp Sci Technol 28(1):40–55

    Article  Google Scholar 

  31. 31.

    Leifsson L, Ko A, Mason WH, Schetz JA, Grossman B, Haftka RT (2013) Multidisciplinary design optimization of blended-wing-body transport aircraft with distributed propulsion. Aerosp Sci Technol 25(1):16–28

    Article  Google Scholar 

  32. 32.

    Fan W, Sun Y, Zhu T, Wen Y (2012) Emissions of HC, CO, NOx, CO2, and SO2 from civil aviation in China in 2010. Atmos Environ 56:52–57

    Article  Google Scholar 

  33. 33.

    Singh V, Sharma SK, Vaibhav S (2012) Identification of dimensions of the optimization of fuel consumption in air transport industry: a literature review. J Energy Technol Policy 2(7):24–33

    Google Scholar 

  34. 34.

    Szodruch J, Grimme W, Blumrich F, Schmid R (2011) Next generation single-aisle aircraft–requirements and technological solutions. J Air Transp Manag 17(1):33–39

    Article  Google Scholar 

  35. 35.

    Drela M (2011) Design Drivers of energy-efficient transport aircraft. SAE Int J Aerosp 4(2):602–618

    Article  Google Scholar 

  36. 36.

    Lee K, Nam T, Perullo C, Mavris DN (2011) Reduced-order modeling of a high-fidelity propulsion system simulation. AIAA J 49(8):1665–1682

    Article  Google Scholar 

  37. 37.

    Ryerson MS, Hansen M (2010) The potential of turboprops for reducing aviation fuel consumption. Transp Res Part D: Transp Environ 15(6):305–314

    Article  Google Scholar 

  38. 38.

    Givoni M, Rietveld P (2010) The environmental implications of airlines’ choice of aircraft size. J Air Transp Manag 16(3):159–167

    Article  Google Scholar 

  39. 39.

    Martinez-Val R, Perez E, Puertas J, Roa J (2010) Optimization of planform and cruise conditions of a transport flying wing. Proc Inst Mech Eng G J Aerosp Eng 224(12):1243–1251

    Article  Google Scholar 

  40. 40.

    Agarwal R (2010) Sustainable (green) aviation: challenges and opportunities. SAE Int J Aerosp 2(1):1–20

    MathSciNet  Article  Google Scholar 

  41. 41.

    Capoccitti S, Khare A, Mildenberger U (2010) Aviation industry-mitigating climate change impacts through technology and policy. J Technol Manag Innov 5(2):66–75

    Article  Google Scholar 

  42. 42.

    Lee DS, Fahey DW, Forster PM, Newton PJ, Wit RC, Lim LL, Sausen R (2009) Aviation and global climate change in the 21st century. Atmos Environ 43(22):3520–3537

    Article  Google Scholar 

  43. 43.

    Morrell P (2009) The potential for European aviation CO2 emissions reduction through the use of larger jet aircraft. J Air Transp Manag 15(4):151–157

    Article  Google Scholar 

  44. 44.

    Lawrence P (2009) Meeting the challenge of aviation emissions: an aircraft industry perspective. Tech Anal Strat Manag 21(1):79–92

    Article  Google Scholar 

  45. 45.

    Parker R (2009) From blue skies to green skies: engine technology to reduce the climate-change impacts of aviation. Tech Anal Strat Manag 21(1):61–78

    Article  Google Scholar 

  46. 46.

    Hall CA, Schwartz E, Hileman JI (2009) Assessment of technologies for the silent aircraft initiative. J Propuls Power 25(6):1153–1162

    Article  Google Scholar 

  47. 47.

    Mazraati M, Alyousif OM (2009) Aviation fuel demand modelling in OECD and developing countries: impacts of fuel efficiency. OPEC Energy Rev 33:23–46

    Article  Google Scholar 

  48. 48.

    Filippone A (2008) Comprehensive analysis of transport aircraft flight performance. Prog Aerosp Sci 44(3):192–236

    Article  Google Scholar 

  49. 49.

    McDonald CF, Massardo AF, Rodgers C, Stone A (2008) Recuperated gas turbine aeroengines. Part III: engine concepts for reduced emissions, lower fuel consumption, and noise abatement. Aircr Eng Aerosp Technol 80(4):408–426

    Article  Google Scholar 

  50. 50.

    McDonald CF, Massardo AF, Rodgers C, Stone A (2008) Recuperated gas turbine aeroengines, part II: engine design studies following early development testing. Aircr Eng Aerosp Technol 80(3):280–294

    Article  Google Scholar 

  51. 51.

    Werner-Westphal W, Heinze PH (2008) Structural sizing for an unconventional, environment-friendly aircraft configuration within integrated conceptual design. Aerosp Sci Technol 12(2):184–194

    Article  Google Scholar 

  52. 52.

    Kehayas N (2007) Aeronautical technology for future subsonic civil transport aircraft. Aircr Eng Aerosp Technol 79(6):600–610

    Article  Google Scholar 

  53. 53.

    Bows A, Anderson KL (2007) Policy clash: Can projected aviation growth be reconciled with the UK Government’s 60 % carbon-reduction target? Transp Policy 14(2):103–110

    Article  Google Scholar 

  54. 54.

    Williams V (2007) The engineering options for mitigating the climate impacts of aviation. Philos Trans R Soc A: Math Phys Eng Sci 365(1861):3047–3059

    Article  Google Scholar 

  55. 55.

    Liew KH, Urip E, Yang SL, Mattingly JD, Marek CJ (2006) Performance cycle analysis of turbofan engine with interstage turbine burner. J Propuls Power 22(2):411–416

    Article  Google Scholar 

  56. 56.

    Najjar YS, Al-Sharif SF (2006) Thermodynamic optimization of the turbofan cycle. Aircr Eng Aerosp Technol 78(6):467–480

    Article  Google Scholar 

  57. 57.

    Akerman J (2005) Sustainable air transport––on track in 2050. Transp Res Part D: Transp Environ 10(2):111–126

    Article  Google Scholar 

  58. 58.

    Antoine NE, Kroo IM (2005) Framework for aircraft conceptual design and environmental performance studies. AIAA J 43(10):2100–2109

    Article  Google Scholar 

  59. 59.

    Sehra AK, Whitlow W Jr (2004) Propulsion and power for 21st century aviation. Prog Aerosp Sci 40(4):199–235

    Article  Google Scholar 

  60. 60.

    Liebeck RH (2004) Design of blended wing body subsonic transport. J Aircr 41(1):10–25

    Article  Google Scholar 

  61. 61.

    Green JE (2003) Civil aviation and the environmental challenge. Aeronaut J 107:281–299

    Google Scholar 

  62. 62.

    Lyantsev OD, Breikin TV, Kulikov GG, Arkov VY (2003) On-line performance optimisation of aero engine control system. Automatica 39(12):2115–2121

    MathSciNet  MATH  Article  Google Scholar 

  63. 63.

    Liu F, Sirignano WA (2001) Turbojet and turbofan engine performance increases through turbine burners. J Propulsion Power 17(3):695–705

    Article  Google Scholar 

  64. 64.

    Bert CW (1999) Range and endurance of turboprop, turbofan, or piston–propeller aircraft having wings with or without camber. Aircr Des 2(4):183–190

    Article  Google Scholar 

  65. 65.

    Sirignano WA, Liu F (1999) Performance increases for gas-turbine engines through combustion inside the turbine. J Propuls Power 15(1):111–118

    Article  Google Scholar 

  66. 66.

    Pant R, Fielding JP (1999) Aircraft configuration and flight profile optimization using simulated annealing. Aircr Des 2(4):239–255

    Article  Google Scholar 

  67. 67.

    Janić M (1999) Aviation and externalities: the accomplishments and problems. Transp Res Part D: Transp Environ 4(3):159–180

    Article  Google Scholar 

  68. 68.

    Nadon LJJP, Kramer SC, King PI (1999) Multidisciplinary optimization in conceptual design of mixed-stream turbofan engines. J Propuls Power 15(1):17–22

    Article  Google Scholar 

  69. 69.

    Vedantham A, Oppenheimer M (1998) Long-term scenarios for aviation: demand and emissions of CO2 and NOX. Energy Policy 26(8):625–641

    Article  Google Scholar 

  70. 70.

    Torenbeek E (1997) Cruise performance and range prediction reconsidered. Prog Aerosp Sci 33(5):285–321

    Article  Google Scholar 

  71. 71.

    Wilson J, Paxson DE (1996) Wave rotor optimization for gas turbine engine topping cycles. J Propuls Power 12(4):778–785

    Article  Google Scholar 

  72. 72.

    Lee SH, Le Dilosquer M, Singh R, Rycroft MJ (1996) Further considerations of engine emissions from subsonic aircraft at cruise altitude. Atmos Environ 30(22):3689–3695

    Article  Google Scholar 

  73. 73.

    Komor P (1995) Reducing energy use in US freight transport. Transp Policy 2(2):119–128

    Article  Google Scholar 

  74. 74.

    Charles R A, & Newman H K (1995) Public policy and technology management: changing the role of government in the operation of air traffic control. Transp J 39–48

  75. 75.

    Sachs G (1992) Optimization of endurance performance. Prog Aerosp Sci 29(2):165–191

    Article  Google Scholar 

  76. 76.

    Rodrigo M-V, Emilio P (1992) Optimum cruise lift coefficient in initial design of jet aircraft. J Aircr 29(4):712–714

    Article  Google Scholar 

  77. 77.

    Klein V (1989) Estimation of aircraft aerodynamic parameters from flight data. Prog Aerosp Sci 26(1):1–77

    MathSciNet  Article  Google Scholar 

  78. 78.

    Szodruch J, Hilbig R (1988) Variable wing camber for transport aircraft. Prog Aerosp Sci 25(3):297–328

    Article  Google Scholar 

  79. 79.

    McCarthy P (1987) Future aircraft fuels and their effect on engine control design. Aircr Eng Aerosp Technol 59(12):9–32

    MathSciNet  Article  Google Scholar 

  80. 80.

    Saravanamuttoo HIH (1987) Modern turboprop engines. Prog Aerosp Sci 24(3):225–248

    Article  Google Scholar 

  81. 81.

    Velikano DP, Stavrov OA, Zamyatin ML (1987) Energy conservation in transportation. Energy 12(10–11):1047–1055

    Article  Google Scholar 

  82. 82.

    Lange RH (1986) A review of advanced turboprop transport aircraft. Prog Aerosp Sci 23(2):151–166

    Article  Google Scholar 

  83. 83.

    Vogelesang LB, Gunnink JW (1986) ARALL: a material challenge for next generation aircraft. Mater Des 7(6):287–300

    Article  Google Scholar 

  84. 84.

    Oates GC (1985) Performance estimation for turbofans with and without mixers. J Propuls Power 1(3):252–256

    Article  Google Scholar 

  85. 85.

    Collins BP (1982) Estimation of aircraft fuel consumption. J Aircr 19(11):969–975

    Article  Google Scholar 

  86. 86.

    Jackson TA (1982) Fuel property effects on air force gas turbine engines-program genesis. J Energy 6(6):376–383

    Article  Google Scholar 

  87. 87.

    Laughlin TF (1982) One manufacturer’s approach to improving jet transport fuel efficiency. Transp Plan Technol 7(3):185–200

    Article  Google Scholar 

  88. 88.

    Satz RW (1980) The solution to the gas turbine temperature problem. Energy Convers Manag 20(1):49–63

    Article  Google Scholar 

  89. 89.

    Tye W (1980) The energy problem — its effect on aircraft design: part 1. Supply and demand. Aircr Eng Aerosp Technol 52(3):9–12

    Article  Google Scholar 

  90. 90.

    Tye W (1980) The energy problem—its effect on aircraft design: part 3 advances in aircraft design. Aircr Eng Aerosp Technol 52(5):2–5

    Article  Google Scholar 

  91. 91.

    Harvey RA, Morris RE, Palfreeman BJ (1979) Aircraft fuel economy the propulsion system contribution. Can Aeronaut Space J 25(1):17–27

    Google Scholar 

  92. 92.

    Wilde G L (1978) Future large civil turbofans and power plants. Aeronaut J 82(811)

  93. 93.

    Denning RM (1978) Energy conserving aircraft from the engine viewpoint. Aircr Eng Aerosp Technol 50(8):27–37

    Article  Google Scholar 

  94. 94.

    Miller MP, Mays RA (1978) Transportation and the U.S. petroleum resource: an aviation perspective. J Energy 2(5):259–268

    Article  Google Scholar 

  95. 95.

    Dow JP, Murphy B, Kohlhoff W (1978) Let’s put fuel efficiency into perspective. Aircr Eng Aerosp Technol 50(7):24–27

    Article  Google Scholar 

  96. 96.

    Galloway TL (1977) Advanced short haul aircraft for high density markets. Acta Astronaut 4(1):15–34

    Article  Google Scholar 

  97. 97.

    Foss RL, Hopkins JP (1977) Potential of turboprop powerplants for fuel conservation. Acta Astronaut 4(1):53–75

    Article  Google Scholar 

  98. 98.

    Archibald RB, Reece WS (1977) The impact of the energy crisis on the demand for fuel efficiency: the case of general aviation. Transp Res 11(3):161–165

    Article  Google Scholar 

  99. 99.

    Whitehead AH Jr (1977) The promise of air cargo—system aspects and vehicle design. Acta Astronaut 4(1):77–98

    Article  Google Scholar 

  100. 100.

    Sweet HS (1977) Short haul transport systems and aircraft technology. Acta Astronaut 4(1):35–52

    Article  Google Scholar 

  101. 101.

    Cleveland FA (1976) Challenge to advanced technology transport aircraft systems. J Aircr 13(10):737–744

    Article  Google Scholar 

  102. 102.

    Constant EW (1973) A Model for technological change applied to the turbojet revolution. Technol Cult 14(4):553–572

    Article  Google Scholar 

  103. 103.

    Alexander AJ, Nelson JR (1973) Measuring technological change: aircraft turbine engines. Technol Forecast Soc Chang 5(2):189–203

    Article  Google Scholar 

  104. 104.

    Lee DS et al (2010) Transport impacts on atmosphere and climate: aviation. Atmos Environ 44(37):4678–4734

    Article  Google Scholar 

  105. 105.

    Michaelis L, Davidson O (1996) GHG mitigation in the transport sector. Energy Policy 24(10):969–984

    Article  Google Scholar 

  106. 106.

    Morrison S A (1984) An economic analysis of aircraft design. J Transp Econ Policy 123–143

  107. 107.

    Sachs G, Christodoulou T (1987) Reducing fuel consumption of subsonic aircraft by optimal cyclic cruise. J Aircr 24(9):616–622

    Article  Google Scholar 

  108. 108.

    Turgut ET et al (2014) Fuel flow analysis for cruise phase of commercial aircraft on domestic routes. Aerosp Sci Technol 37:1–9

    Article  Google Scholar 

  109. 109.

    Simaiakis I, Balakrishnan H, Khadilkar H, Reynolds TG, Hansman RJ, Reilly B, Urlass S (2014) Demonstration of reduced airport congestion through pushback rate control. Transp Res A Policy Pract 66:251–267

    Article  Google Scholar 

  110. 110.

    Reynolds TG (2014) Air traffic management performance assessment using flight inefficiency metrics. Transp Policy 34:63–74

    Article  Google Scholar 

  111. 111.

    Ryerson MS, Hansen M, Bonn J (2014) Time to burn: flight delay, terminal efficiency, and fuel consumption in the national airspace system. Transp Res A Policy Pract 69:286–298

    Article  Google Scholar 

  112. 112.

    Salah K (2014) Environmental impact reduction of commercial aircraft around airports. Less noise and less fuel consumption. Eur Transp Res Rev 6(1):71–84

    Article  Google Scholar 

  113. 113.

    Nangia RK (2006) Efficiency parameters for modern commercial aircraft. Aeronaut J 110(1110):495–510

    Article  Google Scholar 

  114. 114.

    Nangia R K (2006) Operations and aircraft design towards greener civil aviation using air-to-air refueling. Aeronaut J 705–721

  115. 115.

    Alonsoa G, Benitoa A, Lonzab L, Kousoulidoub M (2014) Investigations on the distribution of air transport traffic and CO2 emissions within the European Union. J Air Transport Manag 36:85–93

    Article  Google Scholar 

  116. 116.

    O’Kelly ME (2014) Air freight hubs in the FedEx system: analysis of fuel use. J Air Transport Manag 36:1–12

    Article  Google Scholar 

  117. 117.

    Park Y, O’Kelly ME (2014) Fuel burn rates of commercial passenger aircraft: variations by seat configuration and stage distance. J Transp Geogr 41:137–147

    Article  Google Scholar 

  118. 118.

    Zhang YJ, Xu JX (2013) A novel particles swarm neural network model to optimize aircraft fuel consumption. Adv Mater Res 694:3370–3374

    Article  Google Scholar 

  119. 119.

    Clarke JP, Brooks J, Nagle G, Scacchioli A, White W, Liu SR (2013) Optimized profile descent arrivals at Los Angeles international airport. J Aircr 50(2):360–369

    Article  Google Scholar 

  120. 120.

    Ravizza S, Chen J, Atkin JA, Burke EK, Stewart P (2013) The trade-off between taxi time and fuel consumption in airport ground movement. Public Transport 5(1–2):25–40

    Article  Google Scholar 

  121. 121.

    Delgado L, Prats X, Sridhar B (2013) Cruise speed reduction for ground delay programs: a case study for San Francisco international airport arrivals. Transport Res C: Emerg Technol 36:83–96

    Article  Google Scholar 

  122. 122.

    Fregnani G, Tavares JA, Müller C, Correia AR (2013) A fuel tankering model applied to a domestic airline network. J Adv Transp 47(4):386–398

    Article  Google Scholar 

  123. 123.

    Delgado L, Prats X (2012) En route speed reduction concept for absorbing air traffic flow management delays. J Aircr 49(1):214–224

    Article  Google Scholar 

  124. 124.

    Khadilkar H, Balakrishnan H (2012) Estimation of aircraft taxi fuel burn using flight data recorder archives. Transp Res Part D: Transp Environ 17(7):532–537

    Article  Google Scholar 

  125. 125.

    Lapp M, Wikenhauser F (2012) Incorporating aircraft efficiency measures into the tail assignment problem. J Air Transport Manag 19:25–30

    Article  Google Scholar 

  126. 126.

    Singh V, Sharma SK, Vaibhav S (2012) Modeling the civil aircraft operations for the optimization of fuel consumption in Indian air transport industry. Ind Eng Lett 2(7):20–29

    Google Scholar 

  127. 127.

    Turgut ET, Rosen MA (2012) Relationship between fuel consumption and altitude for commercial aircraft during descent: preliminary assessment with a genetic algorithm. Aerosp Sci Technol 17(1):65–73

    Article  Google Scholar 

  128. 128.

    Mitchell D, Ekstrand H, Prats X, Grönstedt T (2012) An environmental assessment of air traffic speed constraints in the departure phase of flight: a case study at Gothenburg Landvetter Airport, Sweden. Transp Res Part D: Transp Environ 17(8):610–618

    Article  Google Scholar 

  129. 129.

    Nikoleris T, Gupta G, Kistler M (2011) Detailed estimation of fuel consumption and emissions during aircraft taxi operations at Dallas/Fort Worth International Airport. Transp Res Part D: Transp Environ 16(4):302–308

    Article  Google Scholar 

  130. 130.

    Turgut ET (2011) Estimating aircraft fuel flow for a three-degree flight-path-angle descent. J Aircr 48(3):1099–1106

    Article  Google Scholar 

  131. 131.

    Lucia DJ (2011) Cruising in afterburner: air force fuel use and emerging energy policy. Energy Policy 39(9):5356–5365

    Article  Google Scholar 

  132. 132.

    Howitt OJ, Carruthers MA, Smith IJ, Rodger CJ (2011) Carbon dioxide emissions from international air freight. Atmos Environ 45(39):7036–7045

    Article  Google Scholar 

  133. 133.

    Chèze B, Gastineau P, Chevallier J (2011) Forecasting world and regional aviation jet fuel demands to the mid-term (2025). Energy Policy 39(9):5147–5158

    Article  Google Scholar 

  134. 134.

    Rivas D, Lopez-Garcia O, Esteban S, Gallo E (2010) An analysis of maximum range cruise including wind effects. Aerosp Sci Technol 14(1):38–48

    Article  Google Scholar 

  135. 135.

    Zachary DS, Gervais J, Leopold U (2010) Multi-impact optimization to reduce aviation noise and emissions. Transp Res Part D: Transp Environ 15(2):82–93

    Article  Google Scholar 

  136. 136.

    Filippone A (2010) Cruise altitude flexibility of jet transport aircraft. Aerosp Sci Technol 14(4):283–294

    Article  Google Scholar 

  137. 137.

    Senzig DA, Fleming GG, Iovinelli RJ (2009) Modeling of terminal-area airplane fuel consumption. J Aircr 46(4):1089–1093

    Article  Google Scholar 

  138. 138.

    Miyoshi C, Mason KJ (2009) The carbon emissions of selected airlines and aircraft types in three geographic markets. J Air Transp Manag 15(3):138–147

    Article  Google Scholar 

  139. 139.

    Givoni M, Rietveld P (2009) Airline’s choice of aircraft size–explanations and implications. Transp Res A Policy Pract 43(5):500–510

    Article  Google Scholar 

  140. 140.

    Kemp R (2009) Short-haul aviation–under what conditions is it more environmentally benign than the alternatives? Tech Anal Strat Manag 21(1):115–127

    Article  Google Scholar 

  141. 141.

    Pitfield DE, Caves RE, Quddus MA (2010) Airline strategies for aircraft size and airline frequency with changing demand and competition: a simultaneous-equations approach for traffic on the north Atlantic. Journal of Air Transport Management 16(3):151–158

  142. 142.

    Muller C, Santana ESM (2008) Analysis of flight-operating costs and delays: the Sao Paulo terminal maneuvering area. J Air Transp Manag 14(6):293–296

    Article  Google Scholar 

  143. 143.

    Forsyth P (2007) The impacts of emerging aviation trends on airport infrastructure. J Air Transp Manag 13(1):45–52

    Article  Google Scholar 

  144. 144.

    Lee JJ, Waitz IA, Kim BY, Fleming GG, Maurice L, Holsclaw CA (2007) System for assessing aviation’s global emissions (SAGE), part 2: uncertainty assessment. Transp Res Part D: Transp Environ 12(6):381–395

    Article  Google Scholar 

  145. 145.

    Kim BY, Fleming GG, Lee JJ, Waitz IA, Clarke JP, Balasubramanian S, Gupta ML (2007) System for assessing aviation’s global emissions (SAGE), Part 1: model description and inventory results. Transp Res Part D: Transp Environ 12(5):325–346

    Article  Google Scholar 

  146. 146.

    Cames M (2007) Tankering strategies for evading emissions trading in aviation. Clim Pol 7(2):104–120

    Article  Google Scholar 

  147. 147.

    Wei W, Hansen M (2007) Airlines’ competition in aircraft size and service frequency in duopoly markets. Transp Res E Logist Transp Rev 43(4):409–424

    Article  Google Scholar 

  148. 148.

    McLean D (2006) The operational efficiency of passenger aircraft. Aircr Eng Aerosp Technol 78(1):32–38

    Article  Google Scholar 

  149. 149.

    Swan WM, Adler N (2006) Aircraft trip cost parameters: a function of stage length and seat capacity. Transp Res E Logist Transp Rev 42(2):105–115

    Article  Google Scholar 

  150. 150.

    Abdelghany K, Abdelghany A, Raina S (2005) A model for the airlines’ fuel management strategies. J Air Transp Manag 11(4):199–206

    Article  Google Scholar 

  151. 151.

    Simões AF, Schaeffer R (2005) The Brazilian air transportation sector in the context of global climate change: CO2 emissions and mitigation alternatives. Energy Convers Manag 46(4):501–513

    Article  Google Scholar 

  152. 152.

    Cavcar A, Cavcar M (2004) Approximate solutions of range for constant altitude–constant high subsonic speed flight of transport aircraft. Aerosp Sci Technol 8(6):557–567

    MATH  Article  Google Scholar 

  153. 153.

    Cavcar A, Cavcar M (2004) Impact of aircraft performance differences on fuel consumption of aircraft in air traffic management environment. Aircr Eng Aerosp Techn 76(5):502–515

    MATH  Article  Google Scholar 

  154. 154.

    Janic M (2003) Modeling operational, economic and environmental performance of an air transport network. Transp Res Part D: Transp Environ 8(6):415–432

    Article  Google Scholar 

  155. 155.

    Upham P, Thomas C, Gillingwater D, Raper D (2003) Environmental capacity and airport operations: current issues and future prospects. J Transp Manag 9(3):145–151

    Article  Google Scholar 

  156. 156.

    Stolzer AJ (2003) Fuel consumption modeling of transport category aircraft: a flight operations quality assurance (FOQA) analysis. J Air Transp 8(2):3–18

    Google Scholar 

  157. 157.

    Zouein PP, Abillama WR, Tohme E (2002) A multiple period capacitated inventory model for airline fuel management: a case study. J Oper Res Soc 53(4):379–386

    MATH  Article  Google Scholar 

  158. 158.

    Olsthoorn X (2001) Carbon dioxide emissions from international aviation: 1950–2050. J Air Transp Manag 7(2):87–93

    Article  Google Scholar 

  159. 159.

    Wu CL, Caves RE (2000) Aircraft operational costs and turnaround efficiency at airports. J Air Transp Manag 6(4):201–208

    Article  Google Scholar 

  160. 160.

    Leigh R J, Drake L, & Thampapillai D J (1998) An economic analysis of terminal aerodrome forecasts with special reference to Sydney Airport. J Transp Econ Policy 377–392

  161. 161.

    Janic M (1994) Modelling extra aircraft fuel consumption in an en route airspace environment. Transp Plan Technol 18(3):163–186

    MathSciNet  Article  Google Scholar 

  162. 162.

    Stroup JS, Wollmer RD (1992) A fuel management model for the airline industry. Oper Res 40(2):229–237

    Article  Google Scholar 

  163. 163.

    Visser HG (1991) Terminal area traffic management. Prog Aerosp Sci 28(4):323–368

    Article  Google Scholar 

  164. 164.

    Fan HS (1990) Fuel conservation by controlling aircraft ground operations. Transp Plan Technol 15(1):1–11

    Article  Google Scholar 

  165. 165.

    Wolf P, Simon W (1984) Energy consumption in air transport: a contribution to the problem of calculating and comparing energy consumption values of jet-propelled civil aircraft. Transp Rev 4(2):159–171

    Article  Google Scholar 

  166. 166.

    Nash B (1981) A simplified alternative to current airline fuel allocation model. Interfaces 11(1):1–9

    MathSciNet  Article  Google Scholar 

  167. 167.

    Newell GF (1979) Airport capacity and delays. Transp Science 13(3):201–241

    Article  Google Scholar 

  168. 168.

    Hubbard HB (1978) Terminal airspace/ airport congestion delays. Interfaces 8(2):1–14

    Article  Google Scholar 

  169. 169.

    Walters AA (1978) Airports: an economic survey. J Transp Econ Policy 12(2):125–160

    Google Scholar 

  170. 170.

    Darnel DW, Loflin C (1977) National airlines fuel management and allocation model. Interfaces 7(2):1–16

    Article  Google Scholar 

  171. 171.

    Speyer JL (1976) Non-optimality of the steady-state cruise for aircraft. AIAA J 14(11):1604–1610

    MATH  Article  Google Scholar 

  172. 172.

    Taylor P E, McMILLAN C L A U D E, & Glover, F (1976) Substituting ground delays for airborne delays—some unresolved policy questions for the air transport industry. Transp J 85–90

  173. 173.

    Barman JF, Erzberger H (1976) Fixed-range optimum trajectories for short-haul aircraft. J Aircr 13(10):748–754

    Article  Google Scholar 

  174. 174.

    Pilati DA (1974) Energy use and conservation alternatives for airplanes. Transp Res 8(4):433–441

    Article  Google Scholar 

  175. 175.

    Hirst E (1974) Direct and indirect energy use for commercial aviation. Transp Res 8(4–5):427–432

    Article  Google Scholar 

  176. 176.

    Patrón, R S F, Berrou Y, & Botez R M (2014) New methods of optimization of the flight profiles for performance database-modeled aircraft. Proc Inst Mech Eng Part G: J Aerosp Eng 0954410014561772

  177. 177.

    Dancila BD, Botez R, Labour D (2013) Fuel burn prediction algorithm for cruise, constant speed and level flight segments. Aeronaut J 117(1191):491–504

    Article  Google Scholar 

  178. 178.

    Filippone A (2008) Analysis of carbon-dioxide emissions from transport aircraft. J Aircr 45(1):185–197

    Article  Google Scholar 

  179. 179.

    Mazraati M, Faquih YO (2008) Modelling aviation fuel demand: the case of the United States and China. OPEC Energy Rev 32(4):323–342

    Article  Google Scholar 

  180. 180.

    Bartel M, Young TM (2008) Simplified thrust and fuel consumption models for modern two-shaft turbofan engines. J Aircr 45(4):1450–1456

    Article  Google Scholar 

  181. 181.

    Young TM (2008) Fuel-sensitivity analyses for Jet and piston-propeller airplanes. J Aircr 45(2):715–719

    Article  Google Scholar 

  182. 182.

    Torenbeek E, Wittenberg H (1983) Generalized maximum specific range performance. J Aircr 20(7):617–622

    Article  Google Scholar 

  183. 183.

    Drake JW (1974) Social, political and economic constraints on airline fuel optimization. Transp Res 8(4):443–449

    Article  Google Scholar 

  184. 184.

    Dray L, Evans A, Reynolds T, Schäfer A W, Vera-Morales M, & Bosbach W (2014) Airline fleet replacement funded by a carbon tax: an integrated assessment. Transp Policy

  185. 185.

    Rosskopf M, Lehner S, Gollnick V (2014) Economic–environmental trade-offs in long-term airline fleet planning. J Air Transp Manag 34:109–115

    Article  Google Scholar 

  186. 186.

    Khoo H L, & Teoh L E (2014) A bi-objective dynamic programming approach for airline green fleet planning. Transp Res Part D Transp Environ

  187. 187.

    Adler N, Martini G, Volta N (2013) Measuring the environmental efficiency of the global aviation fleet. Transp Res B Methodol 53:82–100

    Article  Google Scholar 

  188. 188.

    Liu W, Lund H, Mathiesen BV (2013) Modelling the transport system in China and evaluating the current strategies towards the sustainable transport development. Energy Policy 58:347–357

    Article  Google Scholar 

  189. 189.

    Naumann M, Suhl L (2013) How does fuel price uncertainty affect strategic airline planning? Oper Res 13(3):343–362

    Google Scholar 

  190. 190.

    Robertson S (2013) High-speed rail’s potential for the reduction of carbon dioxide emissions from short haul aviation: a longitudinal study of modal substitution from an energy generation and renewable energy perspective. Transp Plan Technol 36(5):395–412

    Article  Google Scholar 

  191. 191.

    Ryerson M S, & Kim H (2013) The impact of airline mergers and hub reorganization on aviation fuel consumption. J Clean Prod (In Press)

  192. 192.

    Ryerson MS, Hansen M (2013) Capturing the impact of fuel price on jet aircraft operating costs with Leontief technology and econometric models. Transp Res C Emerg Technol 33:282–296

    Article  Google Scholar 

  193. 193.

    Steven M, Merklein T (2013) The influence of strategic airline alliances in passenger transportation on carbon intensity. J Clean Prod 56:112–120

    Article  Google Scholar 

  194. 194.

    Winchester N, McConnachie D, Wollersheim C, Waitz IA (2013) Economic and emissions impacts of renewable fuel goals for aviation in the US. Transp Res A Policy Pract 58:116–128

    Article  Google Scholar 

  195. 195.

    Winchester N, Wollersheim C, Clewlow R, Jost NC, Paltsev S, Reilly JM, Waitz IA (2013) The impact of climate policy on US aviation. J Transp Econ Policy (JTEP) 47(1):1–15

    Google Scholar 

  196. 196.

    Adler N, Gellman A (2012) Strategies for managing risk in a changing aviation environment. J Transp Manag 21:24–35

    Article  Google Scholar 

  197. 197.

    Tsai WH, Lee KC, Liu JY, Lin HL, Chou YW, Lin SJ (2012) A mixed activity-based costing decision model for green airline fleet planning under the constraints of the European Union Emissions Trading Scheme. Energy 39(1):218–226

    Article  Google Scholar 

  198. 198.

    O’Kelly ME (2012) Fuel burn and environmental implications of airline hub networks. Transp Res Part D: Transp Environ 17(7):555–567

    Article  Google Scholar 

  199. 199.

    Hihara K (2011) Analysis on bargaining about global climate change mitigation in international aviation sector.Transportation. Res Part E: Logist Transp Rev 47(3):342–358

    Article  Google Scholar 

  200. 200.

    Vespermann J, Wald A (2011) Much Ado about Nothing?–An analysis of economic impacts and ecologic effects of the EU-emission trading scheme in the aviation industry. Transp Res A Policy Pract 45(10):1066–1076

    Article  Google Scholar 

  201. 201.

    Nantke HJ (2011) Emissions trading in aviation. Carbon Manag 2(2):127–134

    Article  Google Scholar 

  202. 202.

    Anger A, Köhler J (2010) Including aviation emissions in the EU ETS: much ado about nothing? A review. Transp Policy 17(1):38–46

    Article  Google Scholar 

  203. 203.

    Brueckner JK, Zhang A (2010) Airline emission charges: effects on airfares, service quality, and aircraft design. Transp Res B Methodol 44(8):960–971

    Article  Google Scholar 

  204. 204.

    Yamaguchi K (2010) Voluntary CO2 emissions reduction scheme: analysis of airline voluntary plan in Japan. Transp Res Part D: Transp Environ 15(1):46–50

    Article  Google Scholar 

  205. 205.

    Rothengatter W (2010) Climate change and the contribution of transport: basic facts and the role of aviation. Transp Res Part D: Transp Environ 15(1):5–13

    Article  Google Scholar 

  206. 206.

    Scheelhaase JD (2010) Local emission charges–A new economic instrument at German airports. J Air Transp Manag 16(2):94–99

    Article  Google Scholar 

  207. 207.

    Schaefer M, Scheelhaase J, Grimme W, Maertens S (2010) The economic impact of the upcoming EU emissions trading system on airlines and EU member states—an empirical estimation. Eur Transp Res Rev 2(4):189–200

    Article  Google Scholar 

  208. 208.

    Cook A, Tanner G, Williams V, Meise G (2009) Dynamic cost indexing–managing airline delay costs. J Air transp Manag 15(1):26–35

    Article  Google Scholar 

  209. 209.

    Solomon DS, Hughey KF (2007) A proposed multi criteria analysis decision support tool for international environmental policy issues: a pilot application to emissions control in the international aviation sector. Environ Sci Pol 10(7):645–653

    Article  Google Scholar 

  210. 210.

    Scheelhaase JD, Grimme WG (2007) Emissions trading for international aviation—an estimation of the economic impact on selected European airlines. J Air Transp Manag 13(5):253–263

    Article  Google Scholar 

  211. 211.

    Williams V, Noland RB (2005) Variability of contrail formation conditions and the implications for policies to reduce the climate impacts of aviation. Transp Res Part D: Transp Environ 10(4):269–280

    Article  Google Scholar 

  212. 212.

    Jamin S, Schäfer A, Ben-Akiva ME, Waitz IA (2004) Aviation emissions and abatement policies in the United States: a city-pair analysis. Transp Res Part D: Transp Environ 9(4):295–317

    Article  Google Scholar 

  213. 213.

    Wei W, Hansen M (2003) Cost economics of aircraft size. J Transp Econ Policy 279–296.

  214. 214.

    Carlsson F, Hammar H (2002) Incentive-based regulation of CO2 emissions from international aviation. J Air Transp Manag 8(6):365–372

    Article  Google Scholar 

  215. 215.

    Daniel JI (2002) Benefit-cost analysis of airport infrastructure: the case of taxiways. J Air Transp Manag 8(3):149–164

    Article  Google Scholar 

  216. 216.

    Schipper Y, Rietveld P, Nijkamp P (2001) Environmental externalities in air transport markets. J Air Transp Manag 7(3):169–179

    Article  Google Scholar 

  217. 217.

    Carlsson F (1999) Incentive-based environmental regulation of domestic civil aviation in Sweden. Transp Policy 6(2):75–82

    Article  Google Scholar 

  218. 218.

    Alamdari FE, Brewer D (1994) Taxation policy for aircraft emissions. Transp Policy 1(3):149–159

    Article  Google Scholar 

  219. 219.

    Hayashi P M, & Trapani J M (1987) The impact of energy costs on domestic airline passenger travel. J Transp Econ Policy 73–86

  220. 220.

    Mays RA, Miller MP, Schott JG (1976) Intercity freight fuel utilization at low package densities— airplanes, express trains and trucks. Transp J 16(1):52–75

    Google Scholar 

  221. 221.

    Austin L M, & Hogan W W (1976) Optimizing the procurement of aviation fuels. Manag Sci 515–527

  222. 222.

    Vittek JF Jr (1974) Short haul aviation: will energy limit its future? Transp Res 8(4):451–455

    Article  Google Scholar 

  223. 223.

    Hirst E (1974) Transportation energy conservation: opportunities and policy issues. Transp J 13(3):42–52

    Google Scholar 

  224. 224.

    Soomer MJ, Franx GJ (2008) Scheduling aircraft landings using airlines’ preferences. Eur J Oper Res 190(1):277–291

    MathSciNet  MATH  Article  Google Scholar 

  225. 225.

    Hileman J I, Stratton R W (2014) Alternative jet fuel feasibility. Transp Policy

  226. 226.

    Withers MR et al (2014) Economic and environmental assessment of liquefied natural gas as a supplemental aircraft fuel. Prog Aerosp Sci 66:17–36

    Article  Google Scholar 

  227. 227.

    Pereira SR, Fontes T, Coelho MC (2014) Can hydrogen or natural gas be alternatives for aviation?–A life cycle assessment. Int J Hydrog Energy 39(25):13266–13275

    Article  Google Scholar 

  228. 228.

    Verstraete D (2013) Long range transport aircraft using hydrogen fuel. Int J Hydrog Energy 38(34):14824–14831

    Article  Google Scholar 

  229. 229.

    Yılmaz İ, İlbaş M, Taştan M, Tarhan C (2012) Investigation of hydrogen usage in aviation industry. Energy Convers Manag 63:63–69

    Article  Google Scholar 

  230. 230.

    Chuck CJ, Donnelly J (2014) The compatibility of potential bioderived fuels with Jet A-1 aviation kerosene. Appl Energy 118:83–91

    Article  Google Scholar 

  231. 231.

    Khandelwal B, Karakurt A, Sekaran PR, Sethi V, Singh R (2013) Hydrogen powered aircraft: the future of air transport. Prog Aerosp Sci 60:45–59

    Article  Google Scholar 

  232. 232.

    Boretti A, Dorrington G (2013) Are synthetic liquid hydrocarbon fuels the future of more sustainable aviation in Australia? Int J Hydrog Energy 38(34):14832–14836

    Article  Google Scholar 

  233. 233.

    Wang H, Oehlschlaeger MA (2012) Auto-ignition studies of conventional and Fischer–Tropsch jet fuels. Fuel 98:249–258

    Article  Google Scholar 

  234. 234.

    Kick T, Herbst J, Kathrotia T, Marquetand J, Braun-Unkhoff M, Naumann C, Riedel U (2012) An experimental and modeling study of burning velocities of possible future synthetic jet fuels. Energy 43(1):111–123

    Article  Google Scholar 

  235. 235.

    Hui X, Kumar K, Sung CJ, Edwards T, Gardner D (2012) Experimental studies on the combustion characteristics of alternative jet fuels. Fuel 98:176–182

    Article  Google Scholar 

  236. 236.

    Law CK (2011) Fuel options for next generation chemical propulsion. AIAA J 50(1):19–36

    Article  Google Scholar 

  237. 237.

    Dorbian CS, Wolfe PJ, Waitz IA (2011) Estimating the climate and air quality benefits of aviation fuel and emissions reductions. Atmos Environ 45(16):2750–2759

    Article  Google Scholar 

  238. 238.

    Kumar K, Sung CJ, Hui X (2011) Laminar flame speeds and extinction limits of conventional and alternative jet fuels. Fuel 90(3):1004–1011

    Article  Google Scholar 

  239. 239.

    Blakey S, Rye L, Wilson CW (2011) Aviation gas turbine alternative fuels: a review. Proc Combust Inst 33(2):2863–2885

    Article  Google Scholar 

  240. 240.

    Kumar K, Sung CJ (2010) A comparative experimental study of the autoignition characteristics of alternative and conventional jet fuel/oxidizer mixtures. Fuel 89(10):2853–2863

    Article  Google Scholar 

  241. 241.

    Turgut ET, Rosen MA (2010) Partial substitution of hydrogen for conventional fuel in an aircraft by utilizing unused cargo compartment space. Int J Hydrog Energy 35(3):1463–1473

    Article  Google Scholar 

  242. 242.

    Janic M (2010) Is liquid hydrogen a solution for mitigating air pollution by airports? Int J Hydrog Energy 35(5):2190–2202

    Article  Google Scholar 

  243. 243.

    Nojoumi H, Dincer I, Naterer GF (2009) Greenhouse gas emissions assessment of hydrogen and kerosene-fueled aircraft propulsion. Int J Hydrog Energy 34(3):1363–1369

    Article  Google Scholar 

  244. 244.

    Janic M (2008) The potential of liquid hydrogen for the future ‘carbon-neutral’ air transport system. Transp Res Part D: Transp Environ 13(7):428–435

    Article  Google Scholar 

  245. 245.

    Balster LM, Corporan E, DeWitt MJ, Edwards JT, Ervin JS, Graham JL, Zabarnick S (2008) Development of an advanced, thermally stable, coal-based jet fuel. Fuel Process Technol 89(4):364–378

    Article  Google Scholar 

  246. 246.

    Liu G, Wang L, Qu H, Shen H, Zhang X, Zhang S, Mi Z (2007) Artificial neural network approaches on composition–property relationships of jet fuels based on GC–MS. Fuel 86(16):2551–2559

    Article  Google Scholar 

  247. 247.

    Holley AT, Dong Y, Andac MG, Egolfopoulos FN, Edwards T (2007) Ignition and extinction of non-premixed flames of single-component liquid hydrocarbons, jet fuels, and their surrogates. Proc Combust Inst 31(1):1205–1213

    Article  Google Scholar 

  248. 248.

    Edwards T (2007) Advancements in gas turbine fuels from 1943 to 2005. J Eng Gas Turbines Power 129(1):13–20

    Article  Google Scholar 

  249. 249.

    Dagaut P, Cathonnet M (2006) The ignition, oxidation, and combustion of kerosene: a review of experimental and kinetic modeling. Prog Energy Combust Sci 32(1):48–92

    Article  Google Scholar 

  250. 250.

    Ibarreta AF, Sung CJ (2006) Optimization of Jet-A fuel reforming for aerospace applications. Int J Hydrog Energy 31(8):1066–1078

    Article  Google Scholar 

  251. 251.

    Aksit IM, Moss JB (2005) Model fuels to reproduce the sooting behaviour of aviation kerosene. Fuel 84:239–254

    Article  Google Scholar 

  252. 252.

    Arkoudeas P, Kalligeros S, Zannikos F, Anastopoulos G, Karonis D, Korres D, Lois E (2003) Study of using JP-8 aviation fuel and biodiesel in CI engines. Energy Convers Manag 44(7):1013–1025

    Article  Google Scholar 

  253. 253.

    Wardle DA (2003) Global sale of green air travel supported using biodiesel. Renew Sust Energ Rev 7(1):1–64

    Article  Google Scholar 

  254. 254.

    Edwards T (2003) Liquid fuels and propellants for aerospace propulsion: 1903–2003. J Propuls Power 19(6):1089–1107

    Article  Google Scholar 

  255. 255.

    Maurice LQ, Lander H, Edwards T, Harrison WE III (2001) Advanced aviation fuels: a look ahead via a historical perspective. Fuel 80(5):747–756

    Article  Google Scholar 

  256. 256.

    Lindstedt RP, Maurice LQ (2000) Detailed chemical-kinetic model for aviation fuels. J Propuls Power 16(2):187–195

    Article  Google Scholar 

  257. 257.

    Taylor FA (1997) Hydrogen and other alternative fuels for air and ground transportation. J Air Transp Manag 3(2):102–104

    Article  Google Scholar 

  258. 258.

    Contreras A, Yigit S, Ozay K, Veziroglu TN (1997) Hydrogen as aviation fuel: a comparison with hydrocarbon fuels. Int J Hydrog Energy 22(10–11):1053–1060

    Article  Google Scholar 

  259. 259.

    Pohl HW, Malychev VV (1997) Hydrogen in future civil aviation. Int J Hydrog Energy 22(10):1061–1069

    Article  Google Scholar 

  260. 260.

    Armstrong FW, Allen JE, Denning RM (1997) Fuel related issues concerning the future of aviation. Proc Inst Mech Eng Part G: J Aerosp Eng 211(1):1–11

    Article  Google Scholar 

  261. 261.

    Goodger EM (1996) Jet fuels. Aircr Eng Aerosp Techn 68(5):3–6

    Article  Google Scholar 

  262. 262.

    Berry GD, Pasternak AD, Rambach GD, Ray Smith J, Schock RN (1996) Hydrogen as a future transportation fuel. Energy 21(4):289–303

    Article  Google Scholar 

  263. 263.

    Nagpal JM, Sharma RL, Sagu ML, Tiwari GB (1994) Combustion performance related properties of aviation turbine fuels. Fuel Sci Technol Int 12(4):613–630

    Article  Google Scholar 

  264. 264.

    Heneghan SP, Zabarnick S (1994) Oxidation of jet fuels and the formation of deposit. Fuel 73(1):35–43

    Article  Google Scholar 

  265. 265.

    Pruitt DS, Hardy DR (1994) Analysis of instability deposit to thermal in aviation jet fuels. Fuel Sci Technol Int 12(7–8):1035–1049

    Article  Google Scholar 

  266. 266.

    Veziroglu TN, Barbir F (1992) Hydrogen: the wonder fuel. Int J Hydrog Energy 17(6):391–404

    Article  Google Scholar 

  267. 267.

    Price RO (1991) Liquid hydrogen—an alternative aviation fuel? Int J Hydrog Energy 16(8):557–562

    Article  Google Scholar 

  268. 268.

    Cheng CP, Wang SR, Huang YH, Chang SC, Tang CP (1989) Spectrophotometric studies of storage stability of jet fuel. Fuel 68(2):264–267

    Article  Google Scholar 

  269. 269.

    Zuber K, Bartl P (1989) Quality control of aviation fuels: 1. Automatic simulated distillation and calculation of the vapour pressure of JP-4 aviation fuel (AVTAG) using capillary gas chromatography. Fuel 68(5):659–663

    Article  Google Scholar 

  270. 270.

    Alder HP (1987) Hydrogen in air transportation. Feasibility study for Zurich airport, Switzerland. Report of the Swiss Group. Int J Hydrog Energy 12(8):571–585

    Article  Google Scholar 

  271. 271.

    Mukherjee NL (1987) Comparison of hydrogenated shale oils with standard jet fuels. Fuel Process Technol 17(2):117–129

    MathSciNet  Article  Google Scholar 

  272. 272.

    Marchetti C (1987) The future of hydrogen—an analysis at world level with a special look at air transports. Int J Hydrog Energy 12(2):61–71

    Article  Google Scholar 

  273. 273.

    Wilkinson KG (1983) An airline view of LH2 as a fuel for commercial aircraft. Int J Hydrog Energy 8(10):793–796

    Article  Google Scholar 

  274. 274.

    Veziroglu TN (1980) Next step in aviation. Int J Hydrogen Energy 5:117–118

    Article  Google Scholar 

  275. 275.

    Mikolowsky WT, Noggle LW (1978) The potential of liquid hydrogen as a military aircraft fuel. Int J Hydrog Energy 3(4):449–460

    Article  Google Scholar 

  276. 276.

    Brewer GD (1978) Hydrogen usage in air transportation. Int J Hydrog Energy 3(2):217–229

    Article  Google Scholar 

  277. 277.

    Blazowski WS (1978) Future jet fuel combustion problems and requirements. Prog Energy Combust Sci 4(3):177–199

    Article  Google Scholar 

  278. 278.

    Longwell JP (1977) Synthetic fuels and combustion. Prog Energy Combust Sci 3(2):127–138

    Article  Google Scholar 

  279. 279.

    Brewer GD (1976) Aviation usage of liquid hydrogen fuel—prospects and problems. Int J Hydrog Energy 1(1):65–88

    Article  Google Scholar 

  280. 280.

    Dell RM, Bridger NJ (1975) Hydrogen—the ultimate fuel. Appl Energy 1(4):279–292

    Article  Google Scholar 

  281. 281.

    Knapton JD, Stobie IC, Krier H (1973) Burning rate studies of fuel air mixtures at high pressures. Combust Flame 21(2):211–220

    Article  Google Scholar 

  282. 282.

    Heneghan SP, Martel CR, Williams TF, Ballal DR (1993) Studies of jet fuel thermal stability in a flowing system. J Eng Gas Turbines Power 115(3):480–485

    Article  Google Scholar 

  283. 283.

    Wacker JG (1998) A definition of theory: research guidelines for different theory-building research methods in operations management. J Oper Manag 16(4):361–385

    Article  Google Scholar 

  284. 284.

    Skiena SS (2008) Dynamic Programming. Springer, London, pp 273–315

    Google Scholar 

  285. 285.

    Zingg DW, Nemec M, Pulliam TH (2008) A comparative evaluation of genetic and gradient-based algorithms applied to aerodynamic optimization. Eur J Comput Mech/Revue Européenne de Mécanique Numérique 17(1–2):103–126

    MATH  Article  Google Scholar 

  286. 286.

    Bronson R, Naadimuthu G (1982) Schaum’s outline of theory and problems of operations research. McGraw-Hill, New York

    Google Scholar 

  287. 287.

    Dantzig G B (1998) Linear programming and extensions. Princeton University Press

  288. 288.

    Fister Jr I, Yang X S, Fister I, Brest J, & Fister D (2013) A brief review of nature-inspired algorithms for optimization. arXiv preprint arXiv:1307.4186

  289. 289.

    Zang H, Zhang S, Hapeshi K (2010) A review of nature-inspired algorithms. J Bionic Eng 7:S232–S237

    Article  Google Scholar 

  290. 290.

    Binitha S, Sathya SS (2012) A survey of bio inspired optimization algorithms. Int J Soft Comput Eng 2(2):137–151

    Google Scholar 

  291. 291.

    Gen M, & Cheng R (2000) Genetic algorithms and engineering optimization, vol. 7. John Wiley & Sons

  292. 292.

    Zhang S, Lee CKM, Chan HK, Choy KL, Wu Z (2015) Swarm intelligence applied in green logistics: A literature review. Eng Appl Artif Intell 37:154–169

    Article  Google Scholar 

  293. 293.

    Kachitvichyanukul V (2012) Comparison of three evolutionary algorithms: GA, PSO, and DE. Ind Eng Manag Syst 11(3):215–223

    Google Scholar 

  294. 294.

    Xiao Y, Zhao Q, Kaku I, Xu Y (2012) Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Comput Oper Res 39(7):1419–1431

    MathSciNet  MATH  Article  Google Scholar 

  295. 295.

    Rutenbar RA (1989) Simulated annealing algorithms: an overview. Circuits Devices Mag IEEE 5(1):19–26

    Article  Google Scholar 

  296. 296.

    De Castro L N, & Timmis J (2002) An artificial immune network for multimodal function optimization. In Evolutionary Computation, 2002. CEC’02. Proceedings of the 2002 Congress on (Vol. 1, pp. 699–704) IEEE

  297. 297.

    Meredith JR, Raturi A, Amoako-Gyampah K, Kaplan B (1989) Alternative research paradigms in operations. J Oper Manag 8(4):297–326

    Article  Google Scholar 

  298. 298.

    New York Energy Prices Retail energy price data Accessed 7 Dec 2014

  299. 299.

    Hamilton J D (2011) Historical oil shocks (No. w16790). National Bureau of Economic Research

  300. 300.

    Wright J C (2010) Oil: Demand, Supply and Trends in the United States. University of California Berkeley

  301. 301.

    Owen B (2008) Fuel Efficiency Development and Prediction Main Thematic Area: Climate Change. Omega, Manchester Metropolitan University

  302. 302.

    Bureau of Transportation Statistics. Table 4–21: Energy Intensity of Certificated Air Carriers, All Services (a).

Download references

Author information



Corresponding author

Correspondence to Vedant Singh.

Additional information

This article is part of the Topical Collection on Accessibility and Policy Making

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Singh, V., Sharma, S.K. Fuel consumption optimization in air transport: a review, classification, critique, simple meta-analysis, and future research implications. Eur. Transp. Res. Rev. 7, 12 (2015).

Download citation


  • Air transport industry
  • Meta-analysis
  • Aircraft fuel efficiency
  • Fuel consumption optimization (FCO)