A multi-objective TLBO algorithm for balancing two-sided assembly line with multiple constraints

Dashuang Li, Chaoyong Zhang (), Xinyu Shao and Wenwen Lin
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Dashuang Li: Huazhong University of Science and Technology (HUST)
Chaoyong Zhang: Huazhong University of Science and Technology (HUST)
Xinyu Shao: Huazhong University of Science and Technology (HUST)
Wenwen Lin: Huazhong University of Science and Technology (HUST)

Journal of Intelligent Manufacturing, 2016, vol. 27, issue 4, No 2, 725-739

Abstract: 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.

Keywords: Constrained two-sided assembly line balancing; Improved teaching–learning-based optimization; Multi-objective; Pareto-optimal set (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10845-014-0919-2

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