Meta-Heuristic Algorithms based on Integer Programming for Shelf Space Allocation Problems

Retail shelf space management, which is one of the most complex aspects retailing, can be defined as determining when, where and in what quantities products will displayed dynamically updating display considering changing market conditions. Although it an important problem, research papers that study rectangular arrangement to optimize profit are limited. In this paper, we determine facing units maximize for allocation problem. To solve our two-dimensional develop two matheuristic algorithms by using integer programming genetic algorithm (TP-GA) firefly (TP-ABA) meta-heuristics together. The performances mathheuristics were compared with a real-world dataset from bookstore. TP-GA TP-ABA methods able generate near-optimal solutions average 4.47% 4.57% GAPs, respectively. We also instances up 900 products. These algorithms, successful assignment used similar problems such books bookstore, product families grocery store, or advertisements on websites.