Abstract
Two-sided assembly line is often designed to produce large-sized high-volume products such as cars, trucks and engineering machinery. However, in real-life production process, besides the elementary constraints in the one-sided assembly line, additional constraints, such as zoning constraints, positional constraints and synchronous constraints, may occur in the two-sided assembly line. In this paper, mathematical formulation of balancing multi-objective two-sided assembly line with multiple constraints is established, and some practical objectives, including maximization of the line efficiency, minimization of the smoothness index and minimization of the total relevant costs per product unit (Tcost), have been considered. A novel multi-objective optimization algorithm based on improved teaching–learning-based optimization (ITLBO) algorithm is proposed to obtain the Pareto-optimal set. In the ITLBO algorithm, teacher and learner phases are modified for the discrete problem, and late acceptance hill-climbing is integrated into a novel self-learning phase. A novel merging method is proposed to construct a new population according to the ordering relation between the original and evolutionary population. The proposed algorithm is tested on the benchmark instances and a practical case. Experimental results, compared with the ones computed by other algorithm and in current literature, validate the effectiveness of the proposed algorithm.








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Acknowledgments
The authors would like to thank the referees for their helpful comments. This research is supported by the State Key Program of National Natural Science of China (Grant No. 51035001), National Science Foundation of China (Grant No. 51275190).
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Li, D., Zhang, C., Shao, X. et al. A multi-objective TLBO algorithm for balancing two-sided assembly line with multiple constraints. J Intell Manuf 27, 725–739 (2016). https://doi.org/10.1007/s10845-014-0919-2
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DOI: https://doi.org/10.1007/s10845-014-0919-2