An integrated heuristic and mathematical modelling method to optimize vehicle maintenance schedule under single dead-end track parking and service level agreement

• Published in: Computers & operations research Vol. 132; p. 105261
• Main Authors: Elhüseyni, Murat, Ünal, Ali Tamer
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.08.2021; Pergamon Press Inc
• Subjects: Dead-end track parking; Discrete event systems; Heuristic; Integer programming; Light rail transportation; Linear programming; Maintenance management; Mixed integer; Mixed integer linear programming; Operations research; Preventive maintenance; Schedules; Service level agreement; Urban rail; Vehicle maintenance scheduling; Dead-end track parking; Service level agreement; Mixed integer linear programming; Vehicle maintenance scheduling; Heuristic
• ISSN: 0305-0548; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2021.105261

•A new vehicle maintenance scheduling problem in an urban light rail line.•Corrective jobs are also handled under service level agreement and a dead-end track.•An integrated heuristic and mathematical modelling method to solve the problem.•A simulator is developed to test the devised method and the practical procedure.•As a result of tests, the devised method improves the practical procedure by 74.72%. Inspired by real life maintenance operations, we introduce a vehicle maintenance scheduling problem in an urban light rail line which considers service level agreement (SLA), preventive maintenance cycles and corrective jobs on a single dead-end track. We show that the problem is strongly NP-Hard. Experts in real life make use of a heuristic. Yet, the heuristic calls vehicles in their preventive maintenance cycles as early as possible, which increases the number of maintenance calls in the long term. We build a mixed integer linear programming (MILP) model that handles all aspects of this problem. To improve the quality of the model, we modify it based on the structure of the problem and call MILP2. We enhance the heuristic and name ImprHeur. We introduce ImprHeur to provide a starting solution for the MILP2 model and call ImprHeur + MILP2. We perform computational experiments on random test instances and show that the ImprHeur + MILP2 drastically heightens the solution quality of the MILP model. We define key performance indicators (KPI) to assess the system behavior. We create a discrete-event simulation framework for different problem parameters to test the performance of these heuristics and ImprHeur + MILP2. We conclude that ImprHeur + MILP2 improves the real life heuristic by 74.72% with regard to the objective function value of the MILP model.