  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.
