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The Collaboration of Human-Robot in Mixed-Model Four-Sided Assembly Line Balancing Problem

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Abstract

In the new era, robots play a significant role in assembly lines to assemble different products. In this paper, a combination of humans and robots is used in the mixed-model assembly lines (MMAL) to get better performance from the assembly line. The four-sided assembly line (4-AL) is also considered; that is, in addition to the usual work done on the left and right side, the assembly is also performed on the above and beneath side in some lines. The above-sided tasks are done only by the robot, and it is decided on the other three sides to be done by a robot or a human. The problem’s model has two objectives, minimize the number of mated-station and the cost of utilizing different agents. A small-scale numerical example is solved by the GAMS which indicates the feasibility of the model. An Augmented Multi-Objective particle swarm optimization (AMOPSO) is used to solve the model in large dimensional. AMOPSO has utilized two new methods, local learning strategy and adaptive uniform mutation for the development of the MOPSO algorithm. The Multi-Objective particle swarm optimization (MOPSO) and AMOPSO solutions are compared with each other, and the results show that AMOPSO improves on the responses and has no significant effect on the complexity of solving the problem.

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MSC Code

90B3090C0690C1190C59

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Authors and Affiliations

  1. School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Masoud Rabbani & Seyedeh Zeinab Beladian Behbahan

  2. School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran

    Hamed Farrokhi-Asl

Authors
  1. Masoud Rabbani
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  2. Seyedeh Zeinab Beladian Behbahan
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  3. Hamed Farrokhi-Asl
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Correspondence to Masoud Rabbani.

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Rabbani, M., Behbahan, S.Z.B. & Farrokhi-Asl, H. The Collaboration of Human-Robot in Mixed-Model Four-Sided Assembly Line Balancing Problem. J Intell Robot Syst 100, 71–81 (2020). https://doi.org/10.1007/s10846-020-01177-1

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  • DOI: https://doi.org/10.1007/s10846-020-01177-1

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