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Economic production quantity model with imperfect products and random order frequency under due date and limited storage capacity

Abstract This research is motivated by the fact that production systems are often not perfect and constraints on delivery due date, storage capacity and order frequency are an integral part of many real-world inventory systems. In this regard, this paper deals with a multi-product economic production quantity (EPQ) problem in an imperfect production system with rework where De Novo programming concept has been used in the model formulation. The non-linear programming model constructed here attempts to minimize the total costs of production system where constraints on delivery due date, order frequency as well as storage space are captured in the model. Due to the complexity of the proposed EPQ model to be tackled in real-sized problems, genetic algorithm (GA) and invasive weed optimization (IWO) algorithm are adopted to find near-optimal solutions in reasonable computational times. The Taguchi method for robust parameters design is then used to obtain the optimal level of parameters of the developed algorithms. The practical aspects of the proposed model and the performance of the algorithms for the considered problem are tested through solving a set of randomly generated problem instances. Finally, the results are examined by the statistical analysis and entropy method which specifies the supremacy of developed IWO in comparison with GA.