Multi‐manned assembly line balancing: Workforce synchronization for big data sets through simulated annealing

The assembly of large and complex products such as cars, trucks, and white goods typi-cally involves a huge amount of production resources such as workers, pieces of equipment, and layout areas. In this context, multi‐manned workstations commonly characterize these assembly lines. The simultaneous operatorsʹ activity in the same assembly station suggests considering com-patibility/incompatibility between the different mounting positions, equipment sharing, and worker cooperation. The management of all these aspects significantly increases the balancing problem complexity due to the determination of the start/end times of each task. This paper proposes a new mixed‐integer programming model to simultaneously optimize the line efficiency, the line length, and the workload smoothness. A customized procedure based on a simulated annealing algorithm is developed to effectively solve this problem. The aforementioned procedure is applied to the balancing of the real assembly line of European sports car manufacturers distinguished by 665 tasks and numerous synchronization constraints. The experimental results present remarkable performances obtained by the proposed procedure both in terms of solution quality and computation time. The proposed approach is the practical reference for efficient multi‐manned assembly line design, task assignment, equipment allocation, and mounting position management in the considered industrial fields.