Table 3 Comparison of the selected maintenance planning studies

Consilvio et al. [67]

Maintenance planning

Mixed integer linear programming problem solved with a Matheuristic approach

Tardiness with respect to the soft deadlines and the total completion time

Khalouli et al. [68]

Maintenance scheduling

Mixed-integer linear programming model solved with an ant colony optimisation method

Possession costs, maintenance costs, and penalty costs

Macedo et al. [69]

Maintenance scheduling

Mixed-integer linear programming model solved with a variable neighborhood search

Total operational costs

Zhao et al. [71]

Maintenance scheduling

Mixed-integer linear programming model solved with a genetic algorithm

Cost benefit obtained by combining renewal activities for a track section

Quiroga et al. [72]

Maintenance scheduling

Heuristic algorithm that maximises the defined maintenance process’s objectives

Total reduction track geometry deviation, expected time to failure, and expected geometry deviation at next campaign

Lopes Gerum et al. [73]

Maintenance scheduling

Markov decision process

Maintenance costs

Hamshari et al. [74]

Maintenance scheduling

Gradient descent optimisation

Risk of asset failure and total maintenance costs

Chang et al. [75]

Maintenance scheduling

Hybrid multi-objective optimisation algorithm based on a quantum-behaved particle swarm optimisation

Total cost, window leveling, and resource leveling

Mira et al. [76]

Maintenance scheduling

Integer linear programming mathematical model

Dead-headings, turning times robustness, and number of shuntings to the depot needed to be executed

This paper

Maintenance prioritisation

Ad-hoc prioritisation algorithm

Cost of executing the maintenance intervention after the due date, cost related to postponing in the sequence the maintenance of an asset with a high criticality, and cost of executing in a consecutive order the maintenance of assets located far from each other