Optimization of the short-term labor and task scheduling in the maintenance of regional trains

Optimization of the short-term labor and task scheduling in the maintenance of regional trains

van Leeuwen, Anouk (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Transport Engineering and Logistics)

Duinkerken, M.B. (mentor)
Schott, D.L. (graduation committee)
Horjus, S.E. (mentor)

Delft University of Technology

Marine Technology | Transport Engineering and Logistics

2021-02-19

The Dutch Railways (NS) is the Dutch national passenger railway operator. They own rolling stock that has to be maintained regularly. One of the maintenance facilities executing regular maintenance on regional trains is the maintenance department (OB) in Leidschendam. The aim of this project is to develop a framework for optimizing the productivity in the combined labor and task scheduling in the maintenance of regional trains on a daily perspective to increase the delivery reliability. A mathematical model has been developed based on the Resource-Constrained Project Scheduling Problem. To solve the model a hybrid method combining a genetic algorithm and an annealing-like search heuristic is proposed. Three experiments are performed using real data. During the fist experiment the schedule performance of the algorithm is compared with the performance of the schedules generated by the planners of the OB. The second experiment evaluates different qualification distributions for the available workforce. During the last experiment, the effect of the tardiness cost as introduced in this research is evaluated. The experiments show that the scheduled productivity is increased with an average of 18% per day when the algorithm is used and on average 3 more train units are completely scheduled per day.

Rolling Stock Maintenance Scheduling
Resource-Constrained Project Scheduling Problem
Hybrid Heuristics
Genetic Algorithm
Simulated Annealing

http://resolver.tudelft.nl/uuid:7db77462-e6d7-4116-b00d-1c118f1de4c4

Student theses

master thesis

© 2021 Anouk van Leeuwen