## 8 Conclusions  
This paper considers the assembly process of large products requiring the execution of a great number of tasks, such as cars, trucks, buses, white goods, and automatic machines. Their assembly systems are characterized by lines with multi-manned workstations, which increase the balancing problem complexity. Indeed, the start/end processing time of each task must be determined and some idle time may arise even between tasks assigned to the same worker. Thus, proper workload synchronization is necessary to obtain feasible solutions to this problem. Moreover, other features typifying multi-manned assembly lines are the cooperation between workers to perform tasks together, the relationship of compatibility-incompatibility among mounting positions, and the equipment sharing between workers of the same workstation. Although the here-defined multi-manned assembly lines synchronization problem (MALSP) is crucial for many real production systems, the literature on this field is not abundant. Hence, a new mixed-integer programming model for this specific MALSP is presented. The objective of this model is to found an optimal compromise between maximizing the line efficiency and minimizing both the number of workstations and the workload variation between workers, addressing all the aspects briefly aforementioned. Since this problem is well known as NP-hard, a customized simulated annealing procedure is proposed to solve it efficiently within a reasonable computational time. A case study of a European manufacturer of luxury sports cars is introduced to examine the performance and the validity of and the proposed MIP model and SA procedure. According to the authors' knowledge, this is one of the largest presented in the literature so far since this car assembly requires the execution of 665 tasks. It also includes most of the complexities typifying these industrial environments, as proper synchronization of tasks is required to ensure the precedence technological constraints respect, the absence of overlaps in the use of equipment and mounting positions over the cycle time, and an effective cooperation between workers. The parameters of the SA procedure are appropriately calibrated for this specific case study. The presented solution is obtained after 449 iterations (i.e., feasible solutions generated). It counts 20 workers spread over 10 workstations, and a percentage of 89.85% represents the line efficiency, while the smoothness index is 3% of the average workload per worker. Comparison of these performance indicators with their lower bounds shows that the procedure performs very well and it is suitable for industrial applications with a large number of tasks. In addition, the proposed procedure identifies the synchronized tasks sequence assigned to each worker, the mounting positions occupation, the equipment uses, and the presence of cooperation over the entire cycle time. These results are very useful in practice for companies that aim to design the assembly system of a product, in terms of the number of workstations and equipment allocation, in addition to organizing the workforce as effectively as possible. Further research should integrate this problem by taking into account mixed-model assembly lines since the market requires a large product variety due to mass customization. Finally, given the predominance of manual tasks in assembly processes, the extension of the MALSP to include aspects of workers' ergonomics can be considered as another area of future study in this field.