Constraint programming model for multi-manned assembly line balancing problem

•Multi-Manned Assembly Line Balancing problem (MMALBP) is handled lexicographically.•Constraint Programming (CP) model is proposed to solve the MMALBP optimally or near optimally.•Various search strategies in the CP model is employed and compared to each other.•MMALBP aims to minimize cycle time and the total number of operators, respectively.•Performances of mixed-integer and constraint programming models are compared. In recent years, the multi-manned assembly line has become popular since the large-sized products allow more than one operator working simultaneously on the same product in a workstation. This line usually occurs in large-size products such as cars, buses, trucks, and so on. The multi-manned assembly line offers several advantages, such as fewer number of workers/workstation and less cycle time to improve the performance of the system. However, it has been analyzed by a few papers in literature due to being a relatively new and complex problem. The current study aims to develop an efficient exact solution approach, constraint programming, to solve from small to large-size problems by minimizing the cycle time as a primary objective and the total number of workers as a secondary objective. First, two mixed-integer linear programming (MILP) models are proposed based on previous studies to solve the small test cases of the problem optimally. However, the models are not capable of solving the large-size test instances. Therefore, a constraint programming (CP) model is formulated to address both small and large-size data sets. The results of the CP model are compared with two MILP models and two heuristic algorithms available in the literature. The computational results indicate that the CP model discovers optimal solutions, approximately 90% of all the instances, and small optimality gaps in the remaining instances. It is useful to highlight that the CP model is highly concise and solved by a black-box, commercial solver.