A multi-objective genetic algorithm for a mixed-model assembly U-line balancing type-I problem considering human-related issues, training, and learning
محورهای موضوعی : Mathematical Optimization
Masoud Rabbani
1
(School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran)
Mona Montazeri
2
(School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran)
Hamed Farrokhi-Asl
3
(School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran)
Hamed Rafiei
4
(School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran)
کلید واژه: Objective, Mixed, model assembly lines U, shaped assembly lines Learning and training effect Human, related issues Multi,
چکیده مقاله :
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.
