A Multi-Objective Mathematical Model for Dynamic Cellular Manufacturing System Design under Uncertainty: A Sustainable approach

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Industrial Engineering, Sari Branch, Islamic Azad University, Sari, Iran

3 Department of Mathematics, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran

10.22070/jqepo.2022.15102.1209

Abstract

Using a case study in scaffold production, this paper aimed to develop an integrated multi-objective mathematical model for a dynamic and sustainable cellular manufacturing system under fuzzy uncertainty. In this field, most studies have considered only the economic component or at most two components of sustainable production. Furthermore, with increasing concerns about global warming, environmental issues have become particularly important in manufacturing products and goods. On the other hand, customer satisfaction as one of the social responsibility cases is crucial because customer satisfaction is one of the factors of sustainability of organizations and companies in a competitive environment. Accordingly, sustainable production in this article consists of three components: economic, environmental, and social responsibility. We also subjected sustainable production in a dynamic cellular manufacturing system to fuzzy uncertainty. A sustainable multi-objective mathematical model with objective functions of minimizing costs, minimizing CO2 emissions, and minimizing product shortages (customer satisfaction) was proposed. A case study on scaffolding production was solved in GAMS software with CPLEX solver and augmented Epsilon-constraint method, and its basic variables were investigated to validate the proposed model. Then, due to the high complexity of the cellular manufacturing model, the NSGA-II and MOPSO meta-heuristic algorithms were applied to solve larger problems. As the results can be shown, the NSGA-II algorithm performed better than the MOPSO algorithm. Therefore, the results are then analyzed on the NSGA-II approach. The results indicate the proper performance of the proposed solution approach

Keywords

References

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Journal of Quality Engineering and Production Optimization
Volume 7, Issue 1
May 2022
Pages 98-120

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