Multi-model Assembly Line Balancing and Sequencing with Operators’ Learning and Forgetting Effects under Uncertainty

One of the topics for manufacturers today is to discuss the diversity of customer tastes, which to manage this situation with the least change in products, requires multiple lines that have the necessary flexibility to produce these products. On the other hand, many products require assembly operations. The main purpose of this article is to balance these issues according to the conditions of the workforce and different products.

This paper presents two mathematical models to minimize the number of workstations per given cycle time. In the first model, all parameters are definite. Since customer demand may not be constant and this factor can affect the cycle time, the second model uses a robust approach to this issue.

Analysis of various issues shows that a robust modeling approach provides a more reliable design and allows decision makers to have better assembly based on a better understanding of short-term and long-term conditions under conditions of demand uncertainty.

In this paper, two new mathematical models for assembly line balance are presented. Multi-models in which assembly operations are performed manually by workers and for more accurate planning, the differences that workers have in terms of learning and forgetfulness effect on assembly line balance are considered.