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Hybrid BW-EDAS MCDM methodology for optimal industrial robot selection

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  • Tabasam Rashid
  • Asif Ali
  • Yu-Ming Chu

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.

Suggested Citation

  • Tabasam Rashid & Asif Ali & Yu-Ming Chu, 2021. "Hybrid BW-EDAS MCDM methodology for optimal industrial robot selection," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-18, February.
    • Handle: RePEc:plo:pone00:0246738
    DOI: 10.1371/journal.pone.0246738
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    References listed on IDEAS

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    1. Gupta, Himanshu, 2018. "Evaluating service quality of airline industry using hybrid best worst method and VIKOR," Journal of Air Transport Management, Elsevier, vol. 68(C), pages 35-47.
    2. Geerten Van de Kaa & Daniel Scholten & Jafar Rezaei & Christine Milchram, 2017. "The Battle between Battery and Fuel Cell Powered Electric Vehicles: A BWM Approach," Energies, MDPI, vol. 10(11), pages 1-13, October.
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    4. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
    5. Alireza Alinezhad & Javad Khalili, 2019. "New Methods and Applications in Multiple Attribute Decision Making (MADM)," International Series in Operations Research and Management Science, Springer, number 978-3-030-15009-9, June.
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