Hybrid BW-EDAS MCDM methodology for optimal industrial robot selection

PLoS One. 2021 Feb 9;16(2):e0246738. doi: 10.1371/journal.pone.0246738. eCollection 2021.

Abstract

Industrial robots have different capabilities and specifications according to the required applications. It is becoming difficult to select a suitable robot for specific applications and requirements due to the availability of several types with different specifications of robots in the market. Best-worst method is a useful, highly consistent and reliable method to derive weights of criteria and it is worthy to integrate it with the evaluation based on distance from average solution (EDAS) method that is more applicable and needs fewer number of calculations as compared to other methods. An example is presented to show the validity and usability of the proposed methodology. Comparison of ranking results matches with the well-known distance-based approach, technique for order preference by similarity to ideal solution and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods showing the robustness of the best-worst EDAS hybrid method. Sensitivity analysis performed using eighty to one ratio shows that the proposed hybrid MCDM methodology is more stable and reliable.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Decision Making*
  • Industry*
  • Robotics*

Grants and funding

Dr. Yu-Ming Chu awarded with the research grant by the National Natural Science Foundation of China (Grant Nos. 11971142, 61673169) http://www.nsfc.gov.cn/english/site_1/index.html. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.