Parallel Assembly Lines with Heterogeneous Workforce: A Cost-Driven Mathematical Model and Simulated Annealing Approach

In recent years it has become possible for people, machines and robots to work collaboratively in most production environments. Collaborative working models, which are diversified with the concept of Industry 4.0, have become an important issue in Assembly Line Problems (ALP). In this study, a cost-driven mathematical model which combines Parallel Assembly Line Problems and human and robot collaborative workforce which is an important parameter of Industry 4.0 philosophy is proposed. The mathematical model was tested for new data sets derived from existing data sets in the parallel assembly lines literature and the results were analyzed. Because of the NP-hardness of ALP, a heuristic approach based Simulated Annealing (SA) algorithm was proposed to solve for larger sized problems. For this purpose, the same data sets were solved with SA and the results were compared and analyzed. The results show that the mathematical model gives better results up to Tonge data set (140 task), but feasible solutions can only be obtained by SA algorithm for the rest. Since there are very few studies with heterogeneous workforce in Parallel Assembly Line Balancing Problems (PALBP), this study is believed to provide a significant contribution to related literature.