Retail shelf space allocation considering inventory replenishment

Shelf space allocation to products greatly impacts the profitability in a retail store. This paper makes two contributions to existing retail shelf space allocation problem. First, a nonlinear shelf space allocation model (NLSSAMINV) is developed incorporating inventory replenishment into an existing nonlinear shelf space allocation model (NLSSAM). Second, existing solution methods [dynamic programming algorithm (DPA) and local search heuristic (LSH)] developed to solve NLSSAM are suitably adapted for solving NLSSAMINV. A pre‐processing routine is also developed to reduce the search space in DPA and LSH. It is found from experimental studies that due to pre‐processing routine, DPA and LSH took less CPU time to solve NLSSAMINV than that required for solving NLSSAM.