Maximising store revenues using Tabu search for floor space optimisation

Floor space optimisation (FSO) is a critical revenue management problem commonly encountered by today's retailers. It maximises 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 metaheuristic that exploits the multiple special neighbourhood structures. We also incorporate a mechanism to determine how to combine the multiple neighbourhood 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.