loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Arno van de Ven 1 ; Yingqian Zhang 1 ; Wan-Jui Lee 2 ; Rik Eshuis 1 and Anna Wilbik 1

Affiliations: 1 Eindhoven University of Technology, Eindhoven and The Netherlands ; 2 Maintenance Development, NS (Dutch Railways), Utrecht and The Netherlands

Keyword(s): Planning and Scheduling, Machine Learning, Convolutional Neural Networks, Classification, Local Search.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Formal Methods ; Industrial Applications of AI ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Planning and Scheduling ; Simulation and Modeling ; Soft Computing ; Symbolic Systems

Abstract: Dutch Railways (NS) uses a shunt plan simulator to determine capacities of shunting yards. Central to this simulator is a local search heuristic. Solving this capacity determination problem is very time consuming, as it requires to solve an NP-hard shunting planning problem, and furthermore, the capacity has to determined for a large number of possible scenarios at over 30 shunting yards in The Netherlands. In this paper, we propose to combine machine learning with local search in order to speed up finding shunting plans in the capacity determination problem. The local search heuristic models the activities that take place on the shunting yard as nodes in an activity graph with precedence relations. Consequently, we apply the Deep Graph Convolutional Neural Network, which is a graph classification method, to predict whether local search will find a feasible shunt plan given an initial solution. Our experimental results show our approach can significantly reduce the simulation time in determining the capacity of a given shunting yard. This study demonstrates how machine learning can be used to boost optimization algorithms in an industrial application. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.208.197.243

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
van de Ven, A.; Zhang, Y.; Lee, W.; Eshuis, R. and Wilbik, A. (2019). Determining Capacity of Shunting Yards by Combining Graph Classification with Local Search. In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-350-6; ISSN 2184-433X, SciTePress, pages 285-293. DOI: 10.5220/0007398502850293

@conference{icaart19,
author={Arno {van de Ven}. and Yingqian Zhang. and Wan{-}Jui Lee. and Rik Eshuis. and Anna Wilbik.},
title={Determining Capacity of Shunting Yards by Combining Graph Classification with Local Search},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2019},
pages={285-293},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007398502850293},
isbn={978-989-758-350-6},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Determining Capacity of Shunting Yards by Combining Graph Classification with Local Search
SN - 978-989-758-350-6
IS - 2184-433X
AU - van de Ven, A.
AU - Zhang, Y.
AU - Lee, W.
AU - Eshuis, R.
AU - Wilbik, A.
PY - 2019
SP - 285
EP - 293
DO - 10.5220/0007398502850293
PB - SciTePress