The Maintenance Scheduling and Location Choice Problem for Railway Rolling Stock

TL;DR

This paper introduces a new optimization framework for railway maintenance scheduling and location selection, addressing capacity constraints and improving computational efficiency through innovative cut generation techniques.

Contribution

It develops a novel Logic-Based Benders' Decomposition approach combining MLCP and APP models, with four cut procedures, to optimize maintenance planning in railways.

Findings

The min-cut procedure is best for quick, good solutions.

Heuristic procedures outperform for finding optimal solutions.

Framework demonstrated effectively on Dutch railway scenarios.

Abstract

Due to increasing railway use, the capacity at railway yards and maintenance locations is becoming limiting. Therefore, the scheduling of rolling stock maintenance and the choice regarding optimal locations to perform maintenance is increasingly complicated. This research introduces a Maintenance Scheduling and Location Choice Problem (MSLCP). It simultaneously determines maintenance locations and maintenance schedules of rolling stock, while it also considers the available capacity of maintenance locations, measured in the number of available teams. To solve the MSLCP, an optimization framework based on Logic-Based Benders' Decomposition (LBBD) is proposed by combining two models, the Maintenance Location Choice Problem (MLCP) and the Activity Planning Problem (APP), to assess the capacity of a MLCP solution. Within the LBBD, four cut generation procedures are introduced to improve the computational performance: a naive procedure, two heuristic procedures and the so-called min-cut procedure that aims to exploit the specific characteristics of the problem at hand. The framework is demonstrated on a realistic scenarios from the Dutch railways. It is shown that the best choice for cut generation procedure depends on the objective: when aiming to find a good but not necessarily optimal solution, the min-cut procedure performs best, whereas when aiming for the optimal solution, one of the heuristic procedures is the preferred option. The techniques used in the current research are new to the current field and offer interesting next research opportunities.