Industrial robot selection using stochastic multicriteria acceptability analysis for group decision making

Yelin Fu, Ming Li, Hao Luo, George Q. Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

31 Citations (Scopus)

Abstract

Most of the existing studies investigate the robot selection problem (RSP) in a multiple criteria decision making (MCDM) manner, from the viewpoint of a single person. This contradicts the reality that the robot selection decision is usually made by a committee or a group of experts with different expertise and concerns. For this reason, this paper proposes a group decision making (GDM) methodology for handling multiple criteria robot selection problem (MCRSP), the working process of which is (i) identifying experts, (ii) implementing the standard MCDM process and (iii) achieving a group consensus. Four objective weight determination methods, namely, Shannon entropy, CRITIC, ideal point and distance-based, are proposed to represent four experts. Experts play the role of think tank in supporting the decision maker who is responsible for MCRSP. In light of that the preference among different experts is uncertain, stochastic multicriteria acceptability analysis is then applied to achieve a holistic evaluation results for identifying good compromise choices. Two illustrative examples are presented to demonstrate the effectiveness and validity of our methodology, and compare the results with those obtained through VIKOR and ELECTRE II.

Original languageEnglish
Article number103304
JournalRobotics and Autonomous Systems
Volume122
DOIs
Publication statusPublished - Dec 2019
Externally publishedYes

Keywords

  • Group consensus
  • Multiple criteria decision making
  • Robot selection
  • Stochastic multicriteria acceptability analysis

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • General Mathematics
  • Computer Science Applications

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