<p>Customer demand in the production of building material for equipment groups is often regional. Thus, this paper introduces a batch delivery strategy and considers inventory costs. This work studies the scheduling of distributed assembly flexible workshops in equipment groups, batch delivery, and inventory cost problems to establish a mathematical model. This paper proposes a hybrid differential evolution algorithm with variable neighborhood search (VNS) to address this complex nondeterministic polynomial time hard problem. This algorithm combines the powerful global search capability of the differential evolution algorithm with the efficient local search capability of the VNS algorithm. Additionally, considering the characteristics of the batch delivery strategy, this paper designs a maximum batch strategy based on differences in completion times. Finally, through comparative experiments and data analysis, this work validates the effectiveness and superiority of the proposed algorithm.</p>

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A hybrid differential evolution algorithm for distributed assembly flexible job shop scheduling with batch delivery and inventory

  • ShengWen Zhou,
  • Shuai Han,
  • Ming Yang,
  • BaiGang Du

摘要

Customer demand in the production of building material for equipment groups is often regional. Thus, this paper introduces a batch delivery strategy and considers inventory costs. This work studies the scheduling of distributed assembly flexible workshops in equipment groups, batch delivery, and inventory cost problems to establish a mathematical model. This paper proposes a hybrid differential evolution algorithm with variable neighborhood search (VNS) to address this complex nondeterministic polynomial time hard problem. This algorithm combines the powerful global search capability of the differential evolution algorithm with the efficient local search capability of the VNS algorithm. Additionally, considering the characteristics of the batch delivery strategy, this paper designs a maximum batch strategy based on differences in completion times. Finally, through comparative experiments and data analysis, this work validates the effectiveness and superiority of the proposed algorithm.