An Approach to the High-level Maintenance Planning for EMU Trains Based on Simulated Annealing

TL;DR

This paper presents a novel optimization approach using simulated annealing to improve high-level maintenance scheduling for EMU trains, considering operational costs and capacity constraints.

Contribution

It formulates the EMU maintenance planning as a non-linear 0-1 programming model and proposes a simulated annealing algorithm to solve it effectively.

Findings

The model effectively minimizes mileage loss and operational costs.

The simulated annealing algorithm provides high-quality maintenance schedules.

The approach considers dynamic maintenance capacity and train availability constraints.

Abstract

A high-speed train needs high-level maintenance when its accumulated running mileage or time reaches predefined threshold. The date of delivering an Electric Multiple Unit (EMU) train to maintenance ranges within a time window rather than be a fixed date. Obviously, changing the delivering date always means a different impact on the supply of EMU trains and operation cost. Therefore, the delivering plan has the potential to be optimized. This paper formulates the EMU train high-level maintenance planning problem as a non-linear 0-1 programming model. The model aims at minimizing the mileage loss of all EMU trains with the consideration of the maintenance capacity of the workshop and maintenance ratio at different times. The number of trains under maintenance not only depends on the current maintenance plan, but also influenced by the trains whose maintenance time span from the last planning horizon to current horizon. A state function is established to describe whether a train is under maintenance. By using this function the constraint of restricting the total number of trains that are under maintenance can be formulated reasonably well. Finally, a simulated annealing algorithm is proposed for solving the problem.