Multi-objective optimization of multi-item EOQ model with partial backordering and defective batches and stochastic constraints using MOWCA and MOGWO

In this paper a multi-objective mathematical model is proposed for multi-item EOQ model considering partial backordering and defective supply batches. In order to consider real world situations, different stochastic operational constraints are considered and implemented under uncertainty. For the first time in this research, the rate of partial backordering is considered as decision variable. The aim is to determine time interval between successive supply deliveries, rate of partial backordering and filling rate from stock in order to minimize total inventory costs including holding, backordering and ordering costs, while, minimizing required warehouse space. Due to complexity and nonlinearity of the proposed mathematical model from one hand and importance of providing the decision maker with efficient Pareto optimal solutions, five meta heuristic algorithms as well as one hybrid exact solution method are utilized to solve the problem. To determine the most efficient solution method, the performance of the algorithms is investigated through a deep computational analysis considering different measures including diversity, number of Pareto optimal solutions, spacing and computation time. In addition, single factor ANOVA is utilized to determine significant difference among algorithms at ninety-five percent confidence level to demonstrate the superior algorithm.