Large neighbourhood search for energy-efficient train timetabling

Diarmuid Grimes; Barry Hurley; Deepak Mehta; Barry O'Sullivan

doi:10.1109/ICTAI.2015.122

Large neighbourhood search for energy-efficient train timetabling

Diarmuid Grimes
, Barry Hurley
, Deepak Mehta
, Barry O'Sullivan

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

Abstract

The electric rail sector, like many sectors, is looking for means to reduce its energy consumption and energy cost. In this work we consider the scenario where the utility provider charges based on the maximum consumption over a period. Therefore one wishes to schedule the departure of trains such that the aggregate load is balanced across time periods while satisfying timetabling and resource restrictions. We present an approach which combines the strengths of a number of research areas such as constraint programming, linear programming, mixed-integer programming, and large neighbourhood search. The empirical performance on instances from an ongoing research challenge demonstrates the approach's ability to dramatically reduce the overall energy cost. In addition, we are able to close a number of the instances for which we prove optimality.

Original language	English
Title of host publication	Proceedings - 2015 IEEE 27th International Conference on Tools with Artificial Intelligence, ICTAI 2015
Publisher	IEEE Computer Society
Pages	828-835
Number of pages	8
ISBN (Electronic)	9781509001637
DOIs	https://doi.org/10.1109/ICTAI.2015.122
Publication status	Published - 4 Jan 2016
Event	27th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2015 - Vietri sul Mare, Salerno, Italy Duration: 9 Nov 2015 → 11 Nov 2015

Publication series

Name	Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume	2016-January
ISSN (Print)	1082-3409

Conference

Conference	27th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2015
Country/Territory	Italy
City	Vietri sul Mare, Salerno
Period	9/11/15 → 11/11/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

UCC Futures

Artificial Intelligence and Data Analytics

Keywords

Constraint programming
Energy efficiency
Large neighbourhood search
Mixed-integer programming
Optimisation
Timetabling

Access to Document

10.1109/ICTAI.2015.122

Cite this

Grimes, D., Hurley, B., Mehta, D., & O'Sullivan, B. (2016). Large neighbourhood search for energy-efficient train timetabling. In Proceedings - 2015 IEEE 27th International Conference on Tools with Artificial Intelligence, ICTAI 2015 (pp. 828-835). Article 7372218 (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI; Vol. 2016-January). IEEE Computer Society. https://doi.org/10.1109/ICTAI.2015.122

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title = "Large neighbourhood search for energy-efficient train timetabling",

abstract = "The electric rail sector, like many sectors, is looking for means to reduce its energy consumption and energy cost. In this work we consider the scenario where the utility provider charges based on the maximum consumption over a period. Therefore one wishes to schedule the departure of trains such that the aggregate load is balanced across time periods while satisfying timetabling and resource restrictions. We present an approach which combines the strengths of a number of research areas such as constraint programming, linear programming, mixed-integer programming, and large neighbourhood search. The empirical performance on instances from an ongoing research challenge demonstrates the approach's ability to dramatically reduce the overall energy cost. In addition, we are able to close a number of the instances for which we prove optimality.",

keywords = "Constraint programming, Energy efficiency, Large neighbourhood search, Mixed-integer programming, Optimisation, Timetabling",

author = "Diarmuid Grimes and Barry Hurley and Deepak Mehta and Barry O'Sullivan",

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year = "2016",

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Grimes, D, Hurley, B, Mehta, D & O'Sullivan, B 2016, Large neighbourhood search for energy-efficient train timetabling. in Proceedings - 2015 IEEE 27th International Conference on Tools with Artificial Intelligence, ICTAI 2015., 7372218, Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, vol. 2016-January, IEEE Computer Society, pp. 828-835, 27th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2015, Vietri sul Mare, Salerno, Italy, 9/11/15. https://doi.org/10.1109/ICTAI.2015.122

Large neighbourhood search for energy-efficient train timetabling. / Grimes, Diarmuid; Hurley, Barry; Mehta, Deepak et al.
Proceedings - 2015 IEEE 27th International Conference on Tools with Artificial Intelligence, ICTAI 2015. IEEE Computer Society, 2016. p. 828-835 7372218 (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI; Vol. 2016-January).

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

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N2 - The electric rail sector, like many sectors, is looking for means to reduce its energy consumption and energy cost. In this work we consider the scenario where the utility provider charges based on the maximum consumption over a period. Therefore one wishes to schedule the departure of trains such that the aggregate load is balanced across time periods while satisfying timetabling and resource restrictions. We present an approach which combines the strengths of a number of research areas such as constraint programming, linear programming, mixed-integer programming, and large neighbourhood search. The empirical performance on instances from an ongoing research challenge demonstrates the approach's ability to dramatically reduce the overall energy cost. In addition, we are able to close a number of the instances for which we prove optimality.

AB - The electric rail sector, like many sectors, is looking for means to reduce its energy consumption and energy cost. In this work we consider the scenario where the utility provider charges based on the maximum consumption over a period. Therefore one wishes to schedule the departure of trains such that the aggregate load is balanced across time periods while satisfying timetabling and resource restrictions. We present an approach which combines the strengths of a number of research areas such as constraint programming, linear programming, mixed-integer programming, and large neighbourhood search. The empirical performance on instances from an ongoing research challenge demonstrates the approach's ability to dramatically reduce the overall energy cost. In addition, we are able to close a number of the instances for which we prove optimality.

KW - Constraint programming

KW - Energy efficiency

KW - Large neighbourhood search

KW - Mixed-integer programming

KW - Optimisation

KW - Timetabling

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Grimes D, Hurley B, Mehta D, O'Sullivan B. Large neighbourhood search for energy-efficient train timetabling. In Proceedings - 2015 IEEE 27th International Conference on Tools with Artificial Intelligence, ICTAI 2015. IEEE Computer Society. 2016. p. 828-835. 7372218. (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI). doi: 10.1109/ICTAI.2015.122

Large neighbourhood search for energy-efficient train timetabling

Abstract

Publication series

Conference

UN SDGs

UCC Futures

Keywords

Access to Document

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Cite this