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Cross-dock job assignment problem in space-constrained industrial logistics distribution hubs with a single docking zone

机译:具有单个停靠区的空间受限的工业物流配送中心中的跨站工作分配问题

摘要

This paper addresses a cross-dock operations problem in space-constrained industrial logistics distribution hubs. In these hubs, the number of incoming trucks exceeds the number of docks available, and inbound trucks and orders arrive at random. The solution lies in minimising the waiting time of trucks by coordinating the pick up/delivery sequences of inbound and outbound orders in the storage zones. A mathematical model and a meta-heuristics algorithm, which is based on a genetic algorithm, are developed to address the problem. This research is innovative because the proposed algorithm allows the insertion of inbound orders that arrive at random into the schedule, without causing any significant disturbance to the original outbound order schedule. Computational experiments are conducted to examine the performance of the algorithm under heavy and normal cross-dock conditions. Results show that the algorithm reduces the total makespan of storage operations by 10% to 20% under heavy and normal conditions. The research study benefits manufacturers by increasing cross-docking efficiency in industrial logistics systems characterised by limited temporary storage capacity and the random arrival of inbound trucks.
机译:本文解决了空间受限的工业物流配送中心中的跨码头运营问题。在这些枢纽中,进站卡车的数量超过了可用码头的数量,入站卡车和订单是随机到达的。解决方案在于通过协调存储区中入站和出站订单的接送顺序来最大程度地减少卡车的等待时间。为了解决该问题,开发了一种数学模型和一种基于遗传算法的元启发式算法。这项研究是创新的,因为所提出的算法允许将随机到达的入库单插入到计划中,而不会对原始出库单计划造成任何重大干扰。进行了计算实验,以检查该算法在重载和正常跨坞条件下的性能。结果表明,该算法在恶劣和正常条件下将存储操作的总有效期降低了10%至20%。这项研究通过提高工业物流系统的交叉配送效率而使制造商受益,该体系的特点是临时存储容量有限,并且入境卡车随机到达。

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