论文标题
使用禁忌搜索地板空间优化最大化商店收入
Maximizing Store Revenues using Tabu Search for Floor Space Optimization
论文作者
论文摘要
地板空间优化是零售商通常遇到的关键收入管理问题。它通过将地板空间分配给产品类别来最大化商店收入，这些产品分配给了其最合适的计划图。我们将问题提出为连接的多项选择背包问题，并具有附加的全局约束，并提出了基于禁忌搜索的元元素制度，从而利用了多个特殊的社区结构。我们还结合了一种机制来确定如何结合多个邻域移动。还采用了基于从先前的搜索历史学习的候选列表策略来提高搜索质量。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在合理的时间内解决所有问题。通过计算实验对算法相关组件的个体贡献进行分析。
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.
