Maximising store revenues using Tabu search for floor space optimisation

Floor space optimization is a critical revenue management problem commonly encountered by retailers. It maximizes 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 meta-heuristic that exploits multiple special neighborhood structures. also incorporate mechanism determine how combine moves. A candidate list strategy on learning from prior 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.