Abstract:

Most of the current research on outbound slotting optimization considers the production date of the goods, which is simply to use the first-in first-out principle. How to consider the impact of the production date of the goods on outbound slotting optimization more reasonably is an urgent problem to be solved. For this problem, this paper proposes a outbound slotting optimization model that uses the cost of outbound and the ratio of surplus value of goods as the model factors, and using improved genetic algorithm based on adaptive operator, elite strategy and catastrophe operator with adaptive optimization weights based on warehouse busyness to solve the model. Finally, the actual enterprise production data is used to verify the case. The results show that the improved genetic algorithm is better for the outbound slotting optimization algorithm, and adaptive optimization weights can effectively reduce the risk of goods being damaged or their value falling due to long-term storage and at the same time, it can give priority to outbound efficiency when the warehouse is busy.

Key words: outbound slotting optimization, cost of outbound, the ratio of surplus value of goods, adaptive optimization weights, improved genetic algorithm

摘要：

当前出库货位优化研究对货物生产日期的考虑大多数仅仅是简单地使用先进先出原则，如何更合理考虑货物生产日期对货位优化的影响，是亟待解决的问题。针对此问题，提出以出库代价和货物剩余价值率为优化因素的货位优化模型，并采用基于自适应算子、精英策略和灾变算子的改进遗传算法结合基于仓库繁忙度的自适应优化权重对出库货位优化模型进行求解。采用企业实际生产数据进行验证，实验结果表明采用改进遗传算法的出库货位优化算法效果更优，并且使用基于仓库繁忙度的自适应优化权重，能够有效降低货物因过久存放而造成货物价值下降的风险同时又能在仓库繁忙时优先考虑出库效率。

关键词: 出库货位优化, 出库代价, 货物剩余价值率, 自适应优化权重, 改进遗传算法