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In this study, a mixed-model assembly line (MMAL) balancing problem is applied in a make-to-order (MTO) environment. One of the important problems in MTO systems is identifying the control points, which is considered by designing a control system. Furthermore, the worker assignment problem is defined by considering abilities and operating costs of workers. The proposed model is solved in two stages. First, a multi-objective model by simultaneously minimizing the number of stations and the total cost of the task duplication and workers assignment is considered. The second stage is designing a control system to minimize the work in process.

To solve this problem, a non-dominated sorting genetic algorithm (NSGA-II) is introduced and the proposed model is compared with four multi-objective algorithms (MOAs).

The proposed model is compared with four MOAs, i.e. multi-objective particle swarm optimization, multi-objective ant colony optimization, multi-objective firefly algorithm and multi-objective simulated annealing algorithm. The computational results of the NSGA-II algorithm are superior to the other algorithms, and multi-objective ant colony optimization has the best running time of the four MOA algorithms.

With attention to workers assignment in a MTO environment for the MMAL balancing problem, the present research has several significant implications for the rapidly changing manufacturing challenge.

To the best of the authors’ knowledge, no study has provided for the MMAL balancing problem in a MTO environment considering control points. This study provides the first attempt to fill this research gap. Also, a usual assumption in the literature that common tasks of different models must be assigned to a single station is relaxed and different types of real assignment restrictions like resource restrictions and tasks restrictions are described.