A multi-objective genetic algorithm for a mixed-model assembly U-line balancing type-I problem considering human-related issues, training, and learning

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• 作者：Masoud Rabbani ; Mona Montazeri…
• 关键词：Mixed ; model assembly lines ; U ; shaped assembly lines ; Learning and training effect ; Human ; related issues ; Multi ; objective
• 刊名：Journal of Industrial Engineering International
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：12
• 期：4
• 页码：485-497
• 全文大小：586 KB
• 刊物主题：Industrial and Production Engineering;
• 出版者：Springer Berlin Heidelberg
• ISSN：2251-712X
• 卷排序：12

文摘

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.