Multi-objective assembly line balancing problem with bounded processing times, learning effect, and sequence-dependent setup times

This paper addresses multi-objective optimization of a single-model assembly line balancing problem where the processing times of tasks are unknown variables and the only known information is the lower and upper bounds for processing time of each task. Three objectives are simultaneously considered as follows: (1) minimizing the cycle time, (2) minimizing the equipment cost, and (3) minimizing the smoothness index. In order to reflect the real-world situation adequately, we assume that the task time is dependent on worker(s) (or machine(s)) learning for the same or similar activity and also sequence-dependent setup time exists between tasks. Furthermore, a solution method based on the combination of two multi-objective decision-making methods, weighted and min-max techniques, is proposed to solve the problem. Finally, a numerical example is presented to demonstrate how the proposed methodology provides Pareto optimal solutions.