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

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.