Optimization of a Multi-Item Inventory Model Considering Partial Backordering and Imperfect Products Using Interior-Point, SA and WCA

Developing and optimizing effective inventory systems considering realistic constraints and practical assumptions can help managers remarkably decrease inventory and consequently supply chain costs. In this research, we propose a new variant of the multi-item inventory model taking into account warehouse capacity, on-hand budget constraints, imperfect products in supply deliveries and partial backordering where the products can be converted into perfect products by a local repair shop. To deal with the proposed model, three solution approaches, including interior-point technique, as an exact method, and two metaheuristics based on Simulated Annealing (SA) and Water Cycle Algorithm (WCA), are proposed. Extensive computational experiments are conducted on different sets of instances. Using different measures such as RPD, PRE, and computational time, the performance of the solution approaches is evaluated within different test instances. The results show that the WCA outperforms the two other approaches and leads to the best solutions in the proposed problem.