云服务模式下基于集合覆盖的库存-选址模型

Inventory-Location Model Based on Set Covering in Cloud Service

为了探究云服务模式对库存配置与选址策略的影响,在分析传统集合覆盖模型与云服务模式下集合覆盖模型差异的基础上,构建云服务模式下基于集合覆盖的库存配置与动态选址模型;结合问题特征和约束条件选择有效的遗传算法染色体编码方式,设置合适的适宜度函数,通过选择、交叉、变异来提高算法性能,并利用算例对模型和算法进行验证。结果表明:相较于传统覆盖模型,云服务模式下的覆盖模型能降低成本,且不同问题规模下模型的稳定性好;随着缺货成本、库存持有成本、零售商间距离、运输成本、需求变异系数的增加,云服务模式模型的成本节约更加明显,也更能体现资源共享的优势。

In order to explore the impact of cloud service mode on inventory allocation and location strategy.Based on the analysis of the difference between the traditional set covering model and the cloud service set covering model,this paper builds an inventory allocation and dynamic location model based on set covering in cloud service.This paper combines the problem characteristics and constraints to select an effective genetic algorithm chromosome coding method,sets an appropriate fitness function,improves the performance of the algorithm through selection,crossover,and mutation,and uses examples to verify the model and algorithm.The study found that the coverage model in cloud service can reduce costs compared with the traditional coverage model,and the stability of the model under different scales is good.With the increase of out-of-stock cost,inventory holding cost,distance between retailers,transportation cost,and demand variation coefficient,the cost savings of the cloud service model is more obvious,and it also reflects the advantages of resource sharing.