Differential evolution algorithm for solving RALB problem using cost- and time-based models
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- 作者：J. Mukund Nilakantan ; Izabela Nielsen…
- 关键词：Robotic assembly line balancing ; Assembly line cost ; Cycle time ; Differential evolution
- 刊名：The International Journal of Advanced Manufacturing Technology
- 出版年：2017
- 出版时间：March 2017
- 年：2017
- 卷：89
- 期：1-4
- 页码：311-332
- 全文大小：
- 刊物类别：Engineering
- 刊物主题：Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design;
- 出版者：Springer London
- ISSN：1433-3015
- 卷排序：89
文摘
Assembly process is one of the important aspects in manufacturing industries. Industries are extensively using advanced technologies in assembly lines recently such as robots instead of human labor. Cost associated with human labor such as wages, training, safety, and employee management are eliminated with the help of robots. Investments on assembly lines are cost intensive, and industries continuously need to maximize their utilization. In this paper, a cost-based robotic assembly line balancing (RALB) problem with an objective of minimizing assembly line cost and cycle time is addressed. Moreover, there is no research reported on concurrently optimizing cycle time and assembly line cost for a robotic assembly line system to date. The objective of this paper is to propose models with dual focus on time and cost to minimize the cycle time and total assembly line cost simultaneously. Time-based model with the primary focus to optimize cycle time and the cost-based model with the primary focus to optimize total assembly line cost are developed. Due to NP-hard nature, differential evolution (DE) is the algorithm used to solve the RALB problem. Straight and U-shaped robotic assembly line problems are solved using the proposed algorithm, and the detailed comparisons of the results obtained are presented. While comparing straight and U-shaped RALB problems, assembly line cost and cycle time obtained by U-shaped RALB problems are better than the straight RALB problems. The proposed models have significant managerial implications, and these have been discussed in detail.