TWO-OBJECTIVE OPTIMIZATION FOR INTEGRATING PARTS ORDERING, TWO-STAGE ASSEMBLY FLOW-SHOP SCHEDULING, AND DISTRIBUTION THROUGH ROUTING

This paper presents a two-objective mixed integer linear programming model with conflicting objectives of minimizing the total cost of distribution and holding of products and minimizing the earliness and tardiness penalties. The main innovation of this study is the integration of two different levels of supply chain decision-making, including tactical and operational levels. On this basis, the problems of parts ordering, two-stage assembly flow-shop scheduling and distribution of products through routing were integrated. The Epsilon constraint method was used to solve the model. Given that each of these issues is NP-hard, and their simultaneous integration increases the complexity of the problem, a multi-objective gray wolf optimization (MOGWO) algorithm was used to find optimal Pareto fronts for large-scale problems. The statistical analysis of the MOGWO algorithm and the Epsilon constraint method showed that this algorithm had a significant difference in terms of the number of Pareto solutions and the solving time from the Epsilon constraint method.