Hybrid evolutionary multi-objective algorithms for integrating assembly sequence planning and assembly line balancing

• Published in: International journal of production research Vol. 46; no. 21; pp. 5951 - 5977
• Main Authors: Tseng, H.-E., Chen, M.-H., Chang, C.-C., Wang, W.-P.
• Format: Journal Article
• Language: English; Chinese
• Published: London Taylor & Francis Group 01.11.2008; Washington, DC Taylor & Francis; Taylor & Francis LLC
• Subjects: Algorithms; Applied sciences; Assembly line balancing; Assembly lines; Assembly sequence planning; Decision making models; Decision theory. Utility theory; Exact sciences and technology; Genetic algorithms; Grouping genetic algorithms; Hybrid evolutionary multiple-objective algorithms; Inventory control, production control. Distribution; Operational research and scientific management; Operational research. Management science; Pareto-optimal solutions; Production planning; Studies; Evolutionary algorithm; Replacement problem; Multiobjective programming; Unbalanced conditions; System status; Pareto-optimal solutions; Optimization; Hybrid evolutionary multiple-objective algorithms; Optimal decision; Grouping genetic algorithms; Assembly line balancing; Assembly; Production system; Assembly line; Balancing; Pareto optimum; Decision support system; Decision making; Model selection; Grouping; Assembly sequence planning; Production management; Genetic algorithm; Flexible manufacturing system; Preference; Integrated design; Concurrent engineering; Planning
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207540701362564

Assembly sequence planning (ASP) and assembly line balancing (ALB) play critical roles in designing product assembly systems. In view of the trend of concurrent engineering, pondering simultaneously over these two problems in the development of assembly systems is significant for establishing a manufacturing system. This paper contemplates the assembly tool change and the assembly direction as measurements in ASP; and further, Equal Piles assembly line strategy is adopted and the imbalanced status of the system employed as criteria for the evaluation concerning ALB. Focus of the paper is principally on proposing hybrid evolutionary multiple-objective algorithms (HEMOAs) for solutions with regard to integrate the evolutionary multi-objective optimization and grouping genetic algorithms. The results provide a set of objectives and amend Pareto-optimal solutions to benefit decision makers in the assembly plan. In addition, an implemented decision analytic model supports the preference selection from the Pareto-optimal ones. Finally, the exemplifications demonstrate the effectiveness and performance of the proposed algorithm. The consequences definitely illustrate that HEMOAs search out Pareto-optimal solutions effectively and contribute to references for the flexible change of assembly system design.