Maximizing Store Revenues using Tabu Search for Floor Space Optimization

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 algorithm were conducted with computational experiments.