A simulation-based genetic algorithm for dynamic product assortment of high effect shelf in retailing store

Different shelf has different promotion effect for retail merchandise. High effect shelves are important and sparse resource for retailer. This paper developed a genetic algorithm used for dynamic adjusting product assortment of high effect shelves in retailing setting. A transaction simulation module was designed, which is based on the forecast of future demand intensity. The fitness function value of genetic algorithm can be measured through analyzing simulated transaction data. The simulation module can ensure the product assortment be dynamically adjusted with the transformation of demand.