Solving Train Maintenance Scheduling Problem with Neural Networks and Tree Search

Master thesis (2018)

Authors

S. Zhong Electrical Engineering, Mathematics and Computer Science

Contributors

S.E. Verwer (supervisor 1)
M.M. de Weerdt (supervisor 1)
W.J. Lee (supervisor 1)
J.C. van Gemert (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2018 Shijian Zhong
Neural Networks Supervised Learning Train Maintenance Scheduling Problem Reactive Agent Tree Search
More Info
expand_more

Published Date

10-10-2018

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The Train Maintenance Scheduling Problem (TMSP) is a real-world problem that aims at complete maintenance tasks of trains by scheduling their activities on a service site. Common methods of constructing optimal solutions to this problem are difficult as the problem consists of several highly-related sub-problems. Currently, NS is using a lo- cal search algorithm to provide solutions for the problem. However, it has several deficiencies such as solution randomness and lacking flexibility for rescheduling.

In this research, we investigated the applicability of sequential decision making and supervised learning for solving TMSP. First, we formulate the TMSP problem with a reactive sequential mechanism and define the state and action space. Next, we design a feature representation for states and come up with the best kind of neural network structure through comparisons. Then, we conduct experiments to compare several search strategies with the trained network as the heuristic and find the best one. Fi- nally, we evaluate the solvability of our system and conclude that our approach has a certain capability for solving small-scale problems.

Files

Thesis_final.pdf
(.pdf | 3.36 Mb)