An integrated optimization model and metaheuristics for assortment planning, shelf space allocation, and inventory management of perishable products: A real application

• Published in: PloS one 2022-03, Vol.17 (3), p.e0264186-e0264186
• Main Authors: Sajadi, Seyed Jafar, Ahmadi, Ali
• Format: Article
• Language: English
• Subjects: Algorithms; Biology and Life Sciences; Category management; Commerce; Computer and Information Sciences; Costs; Customer satisfaction; Customers; Engineering and Technology; Evaluation; Evolutionary algorithms; Genetic algorithms; Heuristic methods; Industrial engineering; Inventory; Inventory control; Inventory management; Management; Market positioning; Mathematical models; Medicine and Health Sciences; Methods; Modelling; Models, Theoretical; Optimization; Optimization models; Performance evaluation; Perishable goods; Physical Sciences; Profits; Research and Analysis Methods; Retail stores; Sales; Social Sciences; Solvers; Space allocation; Suppliers; Vibration; Vibration damping
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0264186

Product category management (PCM) plays a pivotal role in today's large stores. PCM manages to answer questions such as assortment planning (AP) and shelf space allocation (SSA). AP problem seeks to determine a list of products and suppliers, while SSA problem tries to design the layout of the selected products in the available shelf space. These problems aim to maximize the retailer sales under different constraints, such as limited purchasing budget, limited space of classes for displaying the products, and having at least a certain number of suppliers. This paper makes an attempt to develop an integrated mathematical model to optimize integrated AP, SSA, and inventory control problem for the perishable products. The objective of the model is to maximize the sales and retail profit, considering the costs of supplier contracting/selecting and ordering, assortment planning, holding, and procurement cost. GAMS BARON solver is hired to solve the proposed model in small and medium scales. However, because the problem is NP-hard, an evolutionary genetic algorithm (GA), and an efficient local search vibration damping optimization (VDO) algorithm are proposed. A real case study is considered to evaluate the effectiveness and capabilities of the model. Besides, some test problems of different sizes are generated and solved by the proposed metaheuristic solvers to confirm the efficient performance of proposed algorithms in solving large-scale instances.