基于Tabu搜索的店面空间优化
Maximizing Store Revenues using Tabu Search for Floor Space Optimization
摘要：
店面空间优化是零售商经常遇到的关键收益管理问题。它最大限度地提高商店的收入最佳分配地板空间的产品类别被分配到他们最合适的货架图。我们制定的问题作为一个连接的多选择背包问题与一个额外的全局约束，并提出了一个基于禁忌搜索的元启发式，利用多个特殊的邻域结构。我们还纳入了一个机制，以确定如何结合多个邻域移动。同时，还提出了一种基于先验搜索历史学习的候选列表策略，以提高搜索质量。一组测试问题的计算测试结果表明，我们的禁忌搜索启发式可以解决所有的问题在合理的时间内。通过计算实验分析了算法中各组成部分的贡献。
英文摘要：
Floor space optimization is a critical revenue management problem commonly encountered by retailers. It maximizes store revenue by optimally allocating floor space to product categories which are assigned to their most appropriate planograms. We formulate the problem as a connected multi-choice knapsack problem with an additional global constraint and propose a tabu search based meta-heuristic that exploits the multiple special neighborhood structures. We also incorporate a mechanism to determine how to combine the multiple neighborhood moves. A candidate list strategy based on learning from prior search history is also employed to improve the search quality. The results of computational testing with a set of test problems show that our tabu search heuristic can solve all problems within a reasonable amount of time. Analyses of individual contributions of relevant components of the algorithm were conducted with computational experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
