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Heuristic Sequence Selection for Inventory Routing Problem

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  • Ahmed Kheiri

    (Department of Management Science, Lancaster University Management School, Lancaster LA1 4YX, United Kingdom)

Abstract

In this paper, an improved sequence-based selection hyper-heuristic method for the Air Liquide inventory routing problem, the subject of the ROADEF/EURO 2016 challenge, is described. The organizers of the challenge have proposed a real-world problem of inventory routing as a difficult combinatorial optimization problem. An exact method often fails to find a feasible solution to such problems. On the other hand, heuristics may be able to find a good quality solution that is significantly better than those produced by an expert human planner. There is a growing interest toward self-configuring automated general-purpose reusable heuristic approaches for combinatorial optimization. Hyper-heuristics have emerged as such methodologies. This paper investigates a new breed of hyper-heuristics based on the principles of sequence analysis to solve the inventory routing problem. The primary point of this work is that it shows the usefulness of the improved sequence-based selection hyper-heuristic, and in particular demonstrates the advantages of using a data science technique of hidden Markov model for the heuristic selection.

Suggested Citation

  • Ahmed Kheiri, 2020. "Heuristic Sequence Selection for Inventory Routing Problem," Transportation Science, INFORMS, vol. 54(2), pages 302-312, March.
    • Handle: RePEc:inm:ortrsc:v:54:y:2020:i:2:p:302-312
    DOI: 10.1287/trsc.2019.0934
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    2. Liu, Aijun & Zhu, Qiuyun & Xu, Lei & Lu, Qiang & Fan, Youqing, 2021. "Sustainable supply chain management for perishable products in emerging markets: An integrated location-inventory-routing model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    3. A. Mor & M. G. Speranza, 2022. "Vehicle routing problems over time: a survey," Annals of Operations Research, Springer, vol. 314(1), pages 255-275, July.
    4. Swan, Jerry & Adriaensen, Steven & Brownlee, Alexander E.I. & Hammond, Kevin & Johnson, Colin G. & Kheiri, Ahmed & Krawiec, Faustyna & Merelo, J.J. & Minku, Leandro L. & Özcan, Ender & Pappa, Gisele L, 2022. "Metaheuristics “In the Large”," European Journal of Operational Research, Elsevier, vol. 297(2), pages 393-406.
    5. Saijun Shao & Kin Keung Lai & Biyun Ge, 2023. "A multi-period inventory routing problem with procurement decisions: a case in China," Annals of Operations Research, Springer, vol. 324(1), pages 1527-1555, May.
    6. Butterwick, Thomas & Kheiri, Ahmed & Lulli, Guglielmo & Gromicho, Joaquim & Kreeft, Jasper, 2023. "Application of selection hyper-heuristics to the simultaneous optimisation of turbines and cabling within an offshore windfarm," Renewable Energy, Elsevier, vol. 208(C), pages 1-16.
    7. Lagos, Felipe & Pereira, Jordi, 2024. "Multi-armed bandit-based hyper-heuristics for combinatorial optimization problems," European Journal of Operational Research, Elsevier, vol. 312(1), pages 70-91.
    8. Jean André & Eric Bourreau & Roberto Wolfler Calvo, 2020. "Introduction to the Special Section: ROADEF/EURO Challenge 2016—Inventory Routing Problem," Transportation Science, INFORMS, vol. 54(2), pages 299-301, March.

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