A hybrid algorithm for allocating tasks, operators, and workstations in multi-manned assembly lines

In a car, there are approximately 30,000 parts produced by many different industries. This is due to the complexity and enormity of the automotive industry chain. The vehicle assembly process comprises welding, painting, prefabrication, and final entire-vehicle assembly. The assembly line has the largest labor force, which should be arranged and balanced to increase production efficiency and reduce labor force requirements. Unlike traditional studies on assembly line balancing problems (ALBPs), this study considers the characteristics of the automotive industry, such as multi-manned workstations, minimization in terms of the numbers of operators and workstations for streamlined production, budget constraints, the optimization of both task and operator allocation among workstations, and the determination of the start/end processing time of each task at different workstations. To address these NP-hard problems, a hybrid heuristic approach that combines the procedure of building feasible balancing solutions and the simulated annealing algorithm is proposed to map out an optimal line balancing plan for multi-manned workstations and to reduce the required workspace for shop operations. Based on the design and analysis of experiments, the effects of the maximum number of allowed operators per workstation and those of the cycle time on ALBP solutions are explored. The optimal combination of algorithm parameters is also determined. The results of this study can serve as a practical reference in planning the allocation of tasks, workstations, and operators in the industry.