A Multiobjective Optimization Model for a Dynamic and Sustainable Cellular Manufacturing System under Uncertainty

For many years, cellular manufacturing has been implemented by owners of manufacturing units. Furthermore, the increasing importance of sustainable development has led manufacturers and managers to consider the concepts of sustainable manufacturing. Sustainable manufacturing includes three components: economic, environmental, and social responsibility. Many research and studies have been conducted in the field of cellular manufacturing, and also in most studies, only the economic component or at most two components of sustainable manufacturing have been taken into account. With increasing concerns about global warming, environmental issues have become particularly important in the production of products and goods. On the other hand, customer satisfaction as one of the aspects of social responsibility is of significant importance. In this research, we put the sustainable manufacturing system in the dynamic cellular manufacturing system under uncertainty (fuzzy parameters). A multiobjective sustainable mathematical model with objective functions of minimizing costs minimizing CO2 emissions and minimizing product shortages (customer satisfaction) was proposed. In order to confirm the validity and accuracy of the proposed model, a small example was solved in GAMS software with CPLEX solver and epsilon constraint method, and its basic variables were investigated. Then, due to the high complexity of the proposed cellular manufacturing model, two meta-heuristic algorithms NSGA-II and MOGWO were used to solve larger problems in MATLAB software. To compare the performance of the two proposed algorithms, ten problems with different dimensions were designed and then the two algorithms were compared with each other based on several performance evaluation indicators. Also, in order to investigate the significance of the difference between the two algorithms based on each index, a statistical analysis was carried out by Minitab software. Taguchi method was also used to adjust the parameters of both algorithms. Based on the analytic results and statistical analysis, the MOGWO algorithm performed better than NSGA-II algorithm and the exact solution method, GAMS software.