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<p>Against the backdrop of intense market competition, integrated scheduling of production and distribution systems (ISPDS) is facing fascinating attention. An extension of the ISPDS is investigated in this paper, which effectively integrates the distributed assembly heterogeneous flowshop scheduling and capacitated vehicle routing problem (DAHFS-CVRP). To tackle this problem, an improved adaptive large neighborhood search (IALNS) algorithm is employed to minimize completion time, travel distance, fixed dispatching cost, and total tardiness time simultaneously. In IALNS, a productive initialization strategy is implemented to construct a group of promising initial solutions. Driven by problem-specific properties, multiple job-related or product-related removal and insertion operators are leveraged to assist IALNS in expanding exploratory potential. Further, seven neighborhood operators are exploited in simulated annealing to increase the efficacy. Likewise, the restart technique is devised to escape from local optima. Finally, the accuracy of the proposed DAHFS-CVRP model is verified. Extensive computational results prove that the IALNS is superior to other comparative algorithms across 108 benchmark instances in tackling DAHFS-CVRP.</p>

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An improved adaptive large neighborhood search algorithm for integrated scheduling of distributed production and distribution systems

  • 期刊论文

  • 发表日期：

    2025年12月8日

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  • Jiawen Deng,
  • Jihui Zhang,
  • Shengxiang Yang

摘要

Against the backdrop of intense market competition, integrated scheduling of production and distribution systems (ISPDS) is facing fascinating attention. An extension of the ISPDS is investigated in this paper, which effectively integrates the distributed assembly heterogeneous flowshop scheduling and capacitated vehicle routing problem (DAHFS-CVRP). To tackle this problem, an improved adaptive large neighborhood search (IALNS) algorithm is employed to minimize completion time, travel distance, fixed dispatching cost, and total tardiness time simultaneously. In IALNS, a productive initialization strategy is implemented to construct a group of promising initial solutions. Driven by problem-specific properties, multiple job-related or product-related removal and insertion operators are leveraged to assist IALNS in expanding exploratory potential. Further, seven neighborhood operators are exploited in simulated annealing to increase the efficacy. Likewise, the restart technique is devised to escape from local optima. Finally, the accuracy of the proposed DAHFS-CVRP model is verified. Extensive computational results prove that the IALNS is superior to other comparative algorithms across 108 benchmark instances in tackling DAHFS-CVRP.