Maximizing Store Revenues using Tabu Search for Floor Space Optimization
Jiefeng Xu, Evren Gul, Alvin Lim

TL;DR
This paper presents a tabu search heuristic for floor space optimization in retail stores, effectively maximizing revenue by intelligently allocating space to product categories using advanced neighborhood strategies.
Contribution
The paper introduces a novel tabu search meta-heuristic tailored for a complex multi-choice knapsack problem with global constraints, incorporating multiple neighborhood structures and a learning-based candidate list.
Findings
Successfully solves all test problems within reasonable time
Effective neighborhood strategies enhance search quality
Component analysis confirms contribution to solution performance
Abstract
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…
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