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A hybrid adaptive large neighborhood search heuristic for lot-sizing with setup times

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  • Muller, Laurent Flindt
  • Spoorendonk, Simon
  • Pisinger, David

Abstract

This paper presents a hybrid of a general heuristic framework and a general purpose mixed-integer programming (MIP) solver. The framework is based on local search and an adaptive procedure which chooses between a set of large neighborhoods to be searched. A mixed integer programming solver and its built-in feasibility heuristics is used to search a neighborhood for improving solutions. The general reoptimization approach used for repairing solutions is specifically suited for combinatorial problems where it may be hard to otherwise design suitable repair neighborhoods. The hybrid heuristic framework is applied to the multi-item capacitated lot sizing problem with setup times, where experiments have been conducted on a series of instances from the literature and a newly generated extension of these. On average the presented heuristic outperforms the best heuristics from the literature, and the upper bounds found by the commercial MIP solver ILOG CPLEX using state-of-the-art MIP formulations. Furthermore, we improve the best known solutions on 60 out of 100 and improve the lower bound on all 100 instances from the literature.

Suggested Citation

  • Muller, Laurent Flindt & Spoorendonk, Simon & Pisinger, David, 2012. "A hybrid adaptive large neighborhood search heuristic for lot-sizing with setup times," European Journal of Operational Research, Elsevier, vol. 218(3), pages 614-623.
    • Handle: RePEc:eee:ejores:v:218:y:2012:i:3:p:614-623
    DOI: 10.1016/j.ejor.2011.11.036
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    15. Alberto Santini & Stefan Ropke & Lars Magnus Hvattum, 2018. "A comparison of acceptance criteria for the adaptive large neighbourhood search metaheuristic," Journal of Heuristics, Springer, vol. 24(5), pages 783-815, October.
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    17. Naccache, Salma & Côté, Jean-François & Coelho, Leandro C., 2018. "The multi-pickup and delivery problem with time windows," European Journal of Operational Research, Elsevier, vol. 269(1), pages 353-362.
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