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

Jinling Zhao1, Yubing Sui2, Yang Xu3

  • 1School of Economics, Shen Zhen Polytechnic, Shenzhen, China.

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Summary

This study introduces a new method for robot selection, enhancing group decision-making with individual preferences (MCGDM-IP). The approach improves industrial robot evaluation and selection by 2.12%.

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Area of Science:

  • Operations Research
  • Industrial Engineering
  • Decision Science

Background:

  • Selecting the optimal industrial robot is complex, involving multiple criteria and diverse decision-maker preferences.
  • Existing methods may not fully capture individual nuances in group decision-making for robot selection.

Purpose of the Study:

  • To propose a novel Multiple Criteria Group Decision Making with Individual Preferences (MCGDM-IP) framework.
  • To enhance the evaluation and selection process for industrial robots by incorporating individual decision-maker preferences.

Main Methods:

  • Utilized four objective criteria elicitation approaches: Shannon entropy, CRITIC, distance-based, and ideal-point.
  • Developed a revised group decision matrix by analyzing preferential differences and priorities among robots.
  • Introduced a satisfaction index to measure the effectiveness of the proposed MCGDM-IP.

Main Results:

  • The MCGDM-IP framework effectively addresses the robot selection problem (RSP).
  • The method demonstrated improved group satisfaction by 2.12% in an illustrative example.
  • Validation through an example from previous literature confirmed the approach's effectiveness and validity.

Conclusions:

  • The proposed MCGDM-IP offers a more satisfactory scheme for evaluating and selecting industrial robots.
  • Incorporating individual preferences significantly enhances group decision-making outcomes in robot selection.
  • The framework provides a robust tool for complex industrial decision-making scenarios.