| Summary: | Abstract In the present day markets, it is essential for organizations that manage their supply chain efficiency to sustain their market share and improve profitability. Optimized inventory control is an integral part of supply chain management. In inventory control problems, determining the ordering times and the order quantities of products are the two strategic decisions either to minimize total costs or to maximize total profits. This paper presents three models of inventory control problems. These three models are deterministic single-product, deterministic multi-product, and stochastic single-product. Due to the high computational complexity, the presented models are solved using the Emperor Penguins Colony (EPC) algorithm as a metaheuristic algorithm and a soft computing method. EPC is a newly published metaheuristic algorithm, which has not yet been employed to solve the inventory control problem. The results of applying the proposed algorithm on the models are compared with the results obtained by nine state-of-the-art and popular metaheuristic algorithms. To justify the proposed EPC, both cost and runtime criteria are considered. To find significant differences between the results obtained by algorithms, statistical analysis is used. The results show that the proposed algorithm for the presented models of inventory control has better solutions, lower cost, and less CPU consumption than other algorithms. Inventory control problem, Metaheuristic algorithm, Nature-inspired, Emperor penguins colony algorithm, Deterministic single-product model, Deterministic multi-product model, Stochastic single-product model
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