Maximisation of line efficiency in mixed-model assembly lines and a case study

In recent years, due to the evolving market and increasing specialized demands, there is a notable surge in competition among companies. Complex model assembly lines, as compared to simple assembly lines, are better equipped to respond to customized customer demands. While numerous studies have been conducted on assembly line balancing in the literature, research specifically addressing the type-E mixed-model assembly line balancing problem is quite limited. In type-E problems, unlike others, the goal is to minimize both cycle time and the number of stations simultaneously, aiming for maximum line efficiency. This study proposes a solution in the form of a mixed-integer mathematical model. Also, a heuristic algorithm is developed for solving large-scale problems. In the developed model, lower and upper limit values for cycle time, denoted as C_min and C_max are provided to offer flexibility to the decision-maker. Additionally, in real-life scenarios, when rebalancing the line, completely reassigning jobs to different stations and the significant changes it brings about are undesirable. Therefore, a new constraint group is added to the model for line rebalancing problems, limiting the number of changes. In addition to the rebalancing problem, the multiple operator situation is integrated into the problem, providing the advantage of determining the number of operators that can be used on the line and increasing line efficiency. The performance of the developed model is tested with different parameters, observing how line efficiency changes and its sensitivity to the number of changes. The proposed model that has been tested with datasets of varying dimensions and case study is capable of rebalancing type-E mixed-model assembly lines, offering the decision-maker alternative solutions under different conditions. According to the results obtained, the heuristic algorithm can achieve over 97% line efficiency in reasonable time for all the problems considered, including large scale problems.