OPTIMIZATION BASED ON FLOWER CUTTING HEURISTICS FOR SPACE ALLOCATION PROBLEM

Context. This research discusses the shelf space allocation problem with vertical and horizontal product categorization, which also includes the products of general and brand assortment as well as products with different storage conditions stored on different shelves and incompatible products stored on the same shelf but no nearby.Objective. The goal is to maximize the profit, product movement, or sales after allocating products on store shelves, defining the shelf for the product and the number of stock-keeping units it has.Method. The research proposes the two variants of heuristics with different sorting rules inside utilized as an approach to solving the retail shelf space allocation problem with horizontal and vertical product categorization. It also covers the application of 13 developed steering parameters dedicated to instances of different sizes, which allows to obtain cost-effective solutions of high quality.Results. The results obtained by heuristics were compared to the optimal solutions given by the commercial CPLEX solver. The effectiveness of the proposed heuristics and the suitability of the control settings were demonstrated by their ability to significantly reduce the number of possible solutions while still achieving the desired outcomes. Both heuristics consistently produced solutions with a quality surpassing 99.80% for heuristic H1 and 99.98% for heuristic H2. Heuristics H1 found 12 optimal solutions, and heuristics H2 found 14 optimal solutions among 15 test instances – highlighting their reliability and efficiency.Conclusions. The specifics of the investigated model can be used by supermarkets, apparel stores, and electronics retailers. By following the explained heuristics stages and the methods of parameter adjustments, the distributor can systematically develop, refine, and deploy a heuristic algorithm that effectively addresses the shelf space allocation problems at hand while being robust and scalable.