Resilient Supplier Selection Based on Fuzzy BWM and GMo-RTOPSIS under Supply Chain Environment

Abstract

Resilient suppliers can reduce supply chain risk, effectively avoid supply chain disruption, and bring profits to enterprises. However, there is no united measuring index system to evaluate the resilient supplier under supply chain environment, and the assessment language sets are usually crisp values. Therefore, in order to fill the research gap, this paper proposes a hybrid method, which combines triangular fuzzy number, the best-worst method (BWM), and the modular TOPSIS in random environments for group decision-making (GMo-RTOPSIS) to solve the above problem. Firstly, the weight of decision-maker is calculated by using fuzzy BWM which can deal with triangular fuzzy numbers. Secondly, triangular fuzzy number is introduced into GMo-RTOPSIS, and combined with fuzzy BWM, alternatives are sorted to select the best resilient supply chain partner. Finally, the feasibility and universality of this method are proved by illustrative examples.

Resilient supplier selection in logistics 4.0 with heterogeneous information

“…It is true that the existing research have shown promising potential of MCDA methods and fuzzy techniques in supplier selection problems (Gan et al, 2019;Haldar et al, 2012Haldar et al, , 2014Hasan, Shohag, Azeem, Paul, & Management, 2015;Jiang, Faiz, & Hassan, 2018). However, there are limitations of the state-of-the-art literatures: (i) to the best of our knowledge, no existing research extends F-MADM framework in supplier evaluation problems leveraging large number of information (time series and graphical information), (ii) no prior study investigated F-MADM approach for evaluating suppliers' performance from resilience and logistics 4.0 perspective, simultaneously, and (iii) it is not clear how fuzzy based TOPSIS can be extended to process inherent uncertainty in decision relevant information associated with both the quantitative and qualitative attributes.…”

An Integrated Fuzzy Model for Selecting Resilient Suppliers in Electronics Industry of Iran

“…John et al (2019) proposed a compound method of triangular fuzzy numbers, the best-worth method, and modular TOPSIS in random environments in order to develop group decision-making processes for the selection of resilient suppliers. The feasibility and globalization of this method was proved with illustrated examples in the end [25]. Yazdani et al (2019) investigates an extended version of the combined compromise solution method with grey numbers, named CoCoSo-G for short, to measure the performance of suppliers in a construction company in Madrid [27].…”

“…However, the applied factors in this research are limited in some aspects. Therefore, the current study has tried to provide a comprehensive review of resilience indicators; so, it can be distinguished from previous research (such as [8,[21][22][23][24], Jiawu Gan et al [25], Amindoust. [26], Sureeyatanapas et al [27], Hasan et al [28], Davoodabadi et al [29], and Hasan et al [30]).…”

Hybrid BW-EDAS MCDM methodology for optimal industrial robot selection

“…Serrai et al [55] evaluated web service selection using BWM and compared it with the results of VIKOR, simple additive weighting, TOPSIS and complex proportional assessment methods. Uncertain extensions of BWM are proposed by different researchers [56][57][58][59][60] these extensions have different advantages but require extensive calculations. Pamucar et al [61] proposed a new full consistency method (FUCOM) for criteria weight calculations showing the method perform better then the BWM and AHP method with respect to consistency and pairwise comparisons but the method require an initial priority of criteria by the decision-maker or expert on the basis of their experience or preference that can confuse DMs to make proper preferences but using BWM DMs require only to select most favorable and least favorable criteria and make best to other and others to worst pairwise comparisons that is much easy task.…”