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Industrial robot selection using a multiple criteria group decision making method with individual preferences

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  • Jinling Zhao
  • Yubing Sui
  • Yang Xu
  • K K Lai

Abstract

This paper proposes a multiple criteria group decision making with individual preferences (MCGDM-IP) to address the robot selection problem (RSP). Four objective criteria elicitation approaches, namely, Shannon entropy approach, CRITIC approach, distance-based approach, and ideal-point approach, are proposed to indicate individual decision makers. A preliminary group decision matrix is therefore formulated. Both preferential differences representing the preference degrees among different robots, and preferential priorities representing the favorite ranking of robots for each individual decision maker, are analyzed to propose a revised group decision matrix. A satisfaction index is developed to manifest the merits of the proposed MCGDM-IP. An illustrative example using the data drawn from previous literature is conducted to indicate the effectiveness and validity of MCGDM-IP. The results demonstrate that the MCGDM-IP could generate a more satisfactory scheme to evaluate and select industrial robots, with an improvement of group satisfactory level as 2.12%.

Suggested Citation

  • Jinling Zhao & Yubing Sui & Yang Xu & K K Lai, 2021. "Industrial robot selection using a multiple criteria group decision making method with individual preferences," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-18, December.
  • Handle: RePEc:plo:pone00:0259354
    DOI: 10.1371/journal.pone.0259354
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    Cited by:

    1. Nazim Ali Khan & Ajay Kumar & Naseem Rao, 2024. "A hybrid robot selection model for efficient decisive support system using fuzzy logic and genetic algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(6), pages 2120-2129, June.

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