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A New SOM Batching Heuristic for Order Picking Systems

机译:用于订单拣配系统的新SOM批次启发式

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Base on the global economics crunch, the enterprise start to reduce the cost in many segments of supply chain in order to reduce the management cost. Therefore, this paper develops a new order batching heuristic (SOMB) for the order picking systems in distribution centers. It applies the idea of Self Organization Map to cluster the orders with higher relativity into a batch to reduce the total order picking distances. The number of SKUs in an order and the number of the same aisles covered for an order batch are the key decision factors. The increasing of SKUs in an order would affect optimum weights of SOMB; also, the weights should be more exactness to enhance the performance of order picking operations. A simulation experiment is executed to verify that the SOMB heuristic can reduce the order picking distances and increasing the utilization of picking vehicle.
机译:在全球经济危机的背景下,企业开始降低供应链许多环节的成本,以降低管理成本。因此,本文针对配送中心的订单拣选系统开发了一种新的订单批处理启发式算法(SOMB)。它采用“自组织映射”的思想将具有较高相关性的订单聚集成一批,以减少总的订单拣选距离。订单中的SKU数量和订单批次中覆盖的相同通道的数量是关键决策因素。 SKU顺序增加会影响SOMB的最佳权重;同样,权重应更加准确,以增强订单拣选操作的性能。通过仿真实验验证了SOMB启发式算法可以减少订单拣选距离并提高拣选车的利用率。

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