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A supervised learning-driven heuristic for solving the facility location and production planning problem

Author

Listed:
  • Wu, Tao
  • Huang, Le
  • Liang, Zhe
  • Zhang, Xiaoning
  • Zhang, Canrong

Abstract

In this study, we propose a supervised learning-driven (SLD) heuristic to solve the capacitated facility location and production planning (CFLPP) problem. Using the solution values derived from linear programming relaxation, Dantzig–Wolfe decomposition, and column generation as features, the SLD heuristic uses a supervised learning approach (i.e., naïve Bayes) to derive an offline-learned oracle on the optimal solution patterns. The oracle and the incumbent feasible solution obtained by a time-oriented decomposition method (i.e., relax-and-fix) are then used to guide a sampling procedure to iteratively create numerous smaller-sized subproblems, which are solved by the relax-and-fix method to gradually improve the solution for the CFLPP problem. Computational results show that the SLD heuristic achieves better solution qualities than the commercial CPLEX solver and several state-of-the-art methods.

Suggested Citation

  • Wu, Tao & Huang, Le & Liang, Zhe & Zhang, Xiaoning & Zhang, Canrong, 2022. "A supervised learning-driven heuristic for solving the facility location and production planning problem," European Journal of Operational Research, Elsevier, vol. 301(2), pages 785-796.
    • Handle: RePEc:eee:ejores:v:301:y:2022:i:2:p:785-796
    DOI: 10.1016/j.ejor.2021.11.020
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    References listed on IDEAS

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    2. Yingyi Huang & Xinyu Wang & Hongyan Chen, 2022. "Location Selection for Regional Logistics Center Based on Particle Swarm Optimization," Sustainability, MDPI, vol. 14(24), pages 1-10, December.

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