Hashemoghli, A., Mahdavi, I., Tajdin, A. (2019). A Novel Robust Possibilistic Cellular Manufacturing Model Considering Worker Skill and Product Quality. Scientia Iranica, 26(1), 538-556. doi: 10.24200/sci.2018.4948.1002
Arash Hashemoghli; Iraj Mahdavi; Ali Tajdin. "A Novel Robust Possibilistic Cellular Manufacturing Model Considering Worker Skill and Product Quality". Scientia Iranica, 26, 1, 2019, 538-556. doi: 10.24200/sci.2018.4948.1002
Hashemoghli, A., Mahdavi, I., Tajdin, A. (2019). 'A Novel Robust Possibilistic Cellular Manufacturing Model Considering Worker Skill and Product Quality', Scientia Iranica, 26(1), pp. 538-556. doi: 10.24200/sci.2018.4948.1002
Hashemoghli, A., Mahdavi, I., Tajdin, A. A Novel Robust Possibilistic Cellular Manufacturing Model Considering Worker Skill and Product Quality. Scientia Iranica, 2019; 26(1): 538-556. doi: 10.24200/sci.2018.4948.1002
A Novel Robust Possibilistic Cellular Manufacturing Model Considering Worker Skill and Product Quality
1Department of Industrial and Systems Engineering, Mazandaran University of Science and Technology, Babol, Iran
2‎Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
Abstract
Design of an appropriate cellular manufacturing system (CMS) leads to system flexibility and production efficiency by using the similarities in the manufacturing process of products. One of the main issues in these systems is to consider product quality level and worker’s skill level in the production process. This study proposes a comprehensive bi-objective possibilistic nonlinear mixed-integer programming model under uncertain environment to design a suitable CMS with aims of minimizing the total costs and total inaction of workers and machines, simultaneously. In this respect, the demand of each product with a specific quality level and linguistic parameters such as product quality level, worker’s skill level and job hardness level on machines are considered under fuzzy environment. To this end, the robust possibilistic programming approach is tailored to cope with fuzzy impute parameters. Finally, a real case study is provided to show the efficiency and applicability of the proposed model. In this respect, the proposed approach could be improved the total costs by 23.6% and the total inaction of workers and machines by 11.7% regarding the real practice. In addition, the performance of the presented model is demonstrated by comparing between the results obtained from the proposed model and actual practice.
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