Maximizing Store Revenues using Tabu Search for Floor Space Optimization
This addresses revenue management for retailers, but it is incremental as it applies an existing meta-heuristic to a specific domain problem.
The paper tackles the floor space optimization problem for retailers by formulating it as a connected multi-choice knapsack problem and proposes a tabu search meta-heuristic with multiple neighborhood structures and a candidate list strategy, achieving solutions for all test problems within reasonable time.
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.