A multi-objective genetic algorithm for a mixed-model assembly U-line balancing type-I problem considering human-related issues, training, and learning - Masoud Rabbani; Mona Montazeri; Hamed Farrokhi-Asl; Hamed Rafiei (Journal of Industrial Engineering International, 2016)
Mixed-model assembly lines are increasingly accepted in many industrial environments to meet the growing trend of greater product variability, diversification of customer demands, and shorter life cycles. In this research, a new mathematical model is presented considering balancing a mixed-model U-line and human-related issues, simultaneously. The objective function consists of two separate components. The first part of the objective function is related to balance problem. In this part, objective functions are minimizing the cycle time, minimizing the number of workstations, and maximizing the line efficiencies. The second part is related to human issues and consists of hiring cost, firing cost, training cost, and salary. To solve the presented model, two well-known multi-objective evolutionary algorithms, namely non-dominated sorting genetic algorithm and multi-objective particle swarm optimization, have been used. A simple solution representation is provided in this paper to encode the solutions. Finally, the computational results are compared and analyzed.
Keywords: 650; Mixed-model assembly lines; U-shaped assembly lines; Learning and training effect; Human-related issues; Multi-objective