Maximizing Store Revenues using Tabu Search for Floor Space Optimization

Floor space optimisation (FSO) is a critical revenue management problem commonly encountered by today's retailers. It maximises store optimally allocating floor to product categories which are assigned their most appropriate planograms. We formulate the as connected multi-choice knapsack with an additional global constraint and propose Tabu search-based metaheuristic that exploits multiple special neighbourhood structures. also incorporate mechanism determine how combine moves. A candidate list strategy based on learning from prior search history employed improve quality. The results of computational testing set test problems show our heuristic can solve all within reasonable amount time. Analyses individual contributions relevant components algorithm were conducted experiments.