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

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

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Article ID: iaor20103415
Volume: 46
Issue: 21
Start Page Number: 5951
End Page Number: 5977
Publication Date: Nov 2008
Journal: International Journal of Production Research
Authors: , , ,
Keywords: programming: multiple criteria, heuristics: genetic algorithms
Abstract:

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.

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