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Variable Neighbourhood Descent with Memory: A Hybrid Metaheuristic for Supermarket Resupply

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Hybrid Metaheuristics (HM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9668))

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Abstract

Supermarket supply chains represent an area in which optimisation of vehicle routes and scheduling can lead to huge cost and environmental savings. As just-in-time ordering practices become more common, traditionally fixed resupply routes and schedules are increasingly unable to meet the demands of the supermarkets. Instead, we model this as a dynamic pickup and delivery problem with soft time windows (PDPSTW). We present the variable neighbourhood descent with memory (VNDM) hybrid metaheuristic (HM) and compare its performance against Q-learning (QL), binary exponential back off (BEBO) and random descent (RD) hyperheuristics on published benchmark and real-world instances of the PDPSTW. We find that VNDM consistently generates the highest quality solutions, with the fewest routes or shortest distances, amongst the methods tested. It is capable of finding the best known solutions to 55 of 176 published benchmarks as well as producing the best results on our real-world data set, supplied by Transfaction Ltd.

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Notes

  1. 1.

    Full results are available at www.cs.york.ac.uk/~philm.

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Acknowledgements

This work has been funded by the Large Scale Complex IT Systems (LSCITS) project of the EPSRC with help from Transfaction Ltd.

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Authors and Affiliations

  1. Department of Computer Science, University of York, York, UK

    Philip Mourdjis, Yujie Chen, Fiona Polack & Peter Cowling

  2. Transfaction Ltd., Cambridge, UK

    Martin Robinson

Authors
  1. Philip Mourdjis
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  2. Yujie Chen
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  3. Fiona Polack
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  4. Peter Cowling
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  5. Martin Robinson
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Corresponding author

Correspondence to Philip Mourdjis .

Editor information

Editors and Affiliations

  1. Universitat Politècnica de Catalunya, Barcelona, Spain

    Maria J. Blesa

  2. University of the Basque Country, San Sebastian, Spain

    Christian Blum

  3. Universiy of Plymouth, Plymouth, United Kingdom

    Angelo Cangelosi

  4. University of Catania, Catania, Italy

    Vincenzo Cutello

  5. Computing, Sheffield Hallam University, SHEFFIELD, United Kingdom

    ALESSANDRO DI NUOVO

  6. University of Catania, Catania, Italy

    Mario Pavone

  7. University of Lille 1, Villeneuve d'Ascq, France

    El-Ghazali Talbi

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Mourdjis, P., Chen, Y., Polack, F., Cowling, P., Robinson, M. (2016). Variable Neighbourhood Descent with Memory: A Hybrid Metaheuristic for Supermarket Resupply. In: Blesa, M., et al. Hybrid Metaheuristics. HM 2016. Lecture Notes in Computer Science(), vol 9668. Springer, Cham. https://doi.org/10.1007/978-3-319-39636-1_3

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  • DOI: https://doi.org/10.1007/978-3-319-39636-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39635-4

  • Online ISBN: 978-3-319-39636-1

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