考虑空间邻接关系的二维货架空间分配问题研究  

A Two-dimensional Shelf Space Allocation Problem Considering Products Spatial Adjacency Relationship

作　　者：郑兰兰 吴锋[1,2] 毕闰芳[1,2] ZHENG Lanlan;WU Feng;BI Runfang(School of Management,Xi'an Jiaotong University,Xi'an,Shaanxi 710049,China;The Key Lab of the Ministry of Education for Process Control and Efficiency Engineering,Ministry of Education,Xi'an,Shaanxi 710049,China)

机构地区：[1]西安交通大学管理学院,陕西西安710049 [2]教育部过程控制与效率工程重点实验室,陕西西安710049

出　　处：《工业工程与管理》2023年第2期90-98,共9页Industrial Engineering and Management

基　　金：国家自然科学基金项目(71871177)。

摘　　要：货架资源的紧缺性使货架空间分配问题成为零售市场中被重点关注的领域。本文在二维货架空间分配模型的基础上,考虑了产品空间邻接关系的影响,建立了混合整数非线性规划模型,并设计了改进型随机密钥遗传算法进行求解,最后使用多个算例进行了广泛验证。在小规模问题中,通过本文设计算法、基准遗传算法和Lingo求解结果比较,发现本文设计算法相对于Lingo在求解效率上有很大提升。在大规模问题中,通过本文设计算法和基准遗传算法比较,发现在收敛时间相近的情况下,本文算法效果更好。通过对加入空间邻接关系效应与否进行对比分析,发现在考虑该效应的情境下,所有算例的利润值都有所提升,并且随着上架产品种类数增多,利润及其增量呈现先增后减的趋势。Shelf space allocation has always been the retail market's focus because of the shortage of shelf resources.First,a mixed-integer non-linear programming model was established for the twodimensional shelf space allocation problem with the influence of the product spatial adjacency relationship(SAR).Secondly,an improved random key genetic algorithm(RKGA)was designed to solve the model.Finally,two-scale instances were simulated to verify it.Results show that the improved RKGA has superior performance over the benchmark genetic algorithm and Lingo.In smallscale instances,the improved RAGA has significantly improved solving accuracy and efficiency compared to Lingo.In large-scale instances,it also performs better when the convergence time is similar to benchmark GA.The profit value of all instances has increased by adding SAR effect.As the number of products increases,the profit and its increment appear to increase first and then decrease.

关 键 词：二维货架 空间分配 空间邻接关系 随机密钥 遗传算法 

分 类 号：C934[经济管理—管理学]