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Spatial and Temporal Optimization for Smart Warehouses with Fast Turnover
Computers & Operations Research ( IF 4.6 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.cor.2020.105091 Zhong-Zhong Jiang , Mingzhong Wan , Zhi Pei , Xuwei Qin
Computers & Operations Research ( IF 4.6 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.cor.2020.105091 Zhong-Zhong Jiang , Mingzhong Wan , Zhi Pei , Xuwei Qin
Abstract With the rapid development of e-commerce and the new retail, the same-day or even same-half day delivery service is provided to compete for market share. Recently, an increased number of e-commerce companies implement the unmanned smart warehouses to improve the logistics efficiency. In order to further reduce the demand response time, a novel picking strategy is designed to firstly split the orders, and then assign the partial orders to different pickers. After all the order segments have been collected, it is shipped to the customer. Due to the inherent complexity of the problem, a two stage optimization model is introduced. In the first stage, an order splitting and batching strategy based on spatial measure is proposed. And a MIP model is constructed to minimize the total picking distance, which is then solved via a column generation based algorithm. In the second stage, the newly formed batches are considered as a priori inputs, which are then assigned to automatic pickers. The picking process is modeled as a special parallel machine scheduling problem with multiple due dates for a single item, which could reduce to a customer order scheduling problem on parallel machines, and it is unary NP-complete. A heuristic method is proposed to obtain an approximate solution. Although the order splitting technique is not new in the logistics industry, the split orders are picked according to a vehicle routing problem, which fails to address the kitting issue for fast turnovers. In the numerical analysis section, the proposed algorithm is validated through extensive testing on various scales of instances. It is observed that the optimality gap for our algorithm is within 9%, and the computation time is around 5 min. Also, the average turnover rate increases by approximately 50% in comparison with the no-splitting policy. In most cases, the average order tardiness decreases by 90% compared with order splitting and no-kitting policy.
中文翻译:
快速周转智能仓库的时空优化
摘要 随着电子商务和新零售的快速发展,提供当日甚至半日送达服务以争夺市场份额。近期,越来越多的电子商务公司实施无人智能仓库,以提高物流效率。为了进一步减少需求响应时间,设计了一种新颖的拣货策略,先拆分订单,然后将部分订单分配给不同的拣货员。收集完所有订单段后,将其运送给客户。由于问题的固有复杂性,引入了两阶段优化模型。在第一阶段,提出了一种基于空间度量的订单拆分和批处理策略。并构建了一个 MIP 模型来最小化总拣选距离,然后通过基于列生成的算法解决。在第二阶段,新形成的批次被视为先验输入,然后分配给自动拣货员。拣货过程被建模为一个特殊的并行机器调度问题,具有单个项目的多个到期日,可以简化为并行机器上的客户订单调度问题,并且它是一元 NP 完全的。提出了一种启发式方法来获得近似解。尽管订单拆分技术在物流行业中并不新鲜,但拆分订单是根据车辆路径问题进行拣配的,无法解决快速周转的配套问题。在数值分析部分,所提出的算法通过在各种规模的实例上进行广泛的测试来验证。据观察,我们算法的最优性差距在 9% 以内,计算时间约为 5 分钟。此外,与不分拆政策相比,平均周转率增加了约 50%。在大多数情况下,与订单拆分和无配套政策相比,平均订单延迟减少了 90%。
更新日期:2021-01-01
中文翻译:
快速周转智能仓库的时空优化
摘要 随着电子商务和新零售的快速发展,提供当日甚至半日送达服务以争夺市场份额。近期,越来越多的电子商务公司实施无人智能仓库,以提高物流效率。为了进一步减少需求响应时间,设计了一种新颖的拣货策略,先拆分订单,然后将部分订单分配给不同的拣货员。收集完所有订单段后,将其运送给客户。由于问题的固有复杂性,引入了两阶段优化模型。在第一阶段,提出了一种基于空间度量的订单拆分和批处理策略。并构建了一个 MIP 模型来最小化总拣选距离,然后通过基于列生成的算法解决。在第二阶段,新形成的批次被视为先验输入,然后分配给自动拣货员。拣货过程被建模为一个特殊的并行机器调度问题,具有单个项目的多个到期日,可以简化为并行机器上的客户订单调度问题,并且它是一元 NP 完全的。提出了一种启发式方法来获得近似解。尽管订单拆分技术在物流行业中并不新鲜,但拆分订单是根据车辆路径问题进行拣配的,无法解决快速周转的配套问题。在数值分析部分,所提出的算法通过在各种规模的实例上进行广泛的测试来验证。据观察,我们算法的最优性差距在 9% 以内,计算时间约为 5 分钟。此外,与不分拆政策相比,平均周转率增加了约 50%。在大多数情况下,与订单拆分和无配套政策相比,平均订单延迟减少了 90%。