A robust optimization approach for a cellular manufacturing system considering skill-leveled operators and multi-functional machines

One of the most critical issues in manufacturing systems is the operator management. In this paper, the operator assignment problem is studied within a cellular manufacturing system. The most important novelty of this research is the consideration of operator learning and forgetting effects simultaneously. The skill level of an operator can be increased/decreased based on the time spent on a machine. Moreover, the issues related to operators like hiring, firing, and salaries are considered in the proposed model. The parameters are considered to be uncertain in this model, and a robust optimization approach is developed to handle it. Using this approach, the model solution remains feasible (or even optimal) for different levels of parameter uncertainty. To verify and validate the proposed model, some numerical instances are randomly generated and solved using GAMS. A statistical analysis is also conducted on the results of the objective function values of linear and nonlinear models, followed by some managerial insights. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper investigates the operator assignment problem in cellular manufacturing systems, with a focus on operator learning and forgetting effects, as well as handling uncertain parameters. Numerical instances solving and statistical analysis of the model were conducted to gain managerial insights.