Two-Dimensional Assortment and Shelf-Space Allocation Problem

With growing product proliferation and various demand effects, retailers face the challenge of finding an effective and efficient product assortment and shelf-space allocation. These two planning problems are interrelated, particularly when shelf space is limited. Therefore, it is essential to consider these planning problems jointly to maximize revenue. Moreover, many existing models consider shelf space as one-dimensional. However, since the dimensions of both shelves and products vary in reality, solutions obtained from these one-dimensional models are unable to cope with real-life situations. To approximate the actual shelf dimensions, this paper presents an integrated assortment and shelf-space allocation model for two-dimensional shelves. To solve the model, we proposed three heuristics and tested them on randomly generated data sets. Among these three heuristics, the mixed heuristic proves to be the best approach, efficiently yielding near-optimal solutions within a very short runtime. Comparisons between the heuristics and the optimization model demonstrate that the mixed heuristic is competitive with other heuristics and the optimization model, especially for large-scale problems. We expect that our heuristics will assist retailers in making decisions on product assortment and shelf-space allocation.