BWM Integrated Intuitionistic Fuzzy Approach for Sustainable Transportation Service Provider Selection


DURSUN M.

JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING, vol.37, no.3-4, pp.277-294, 2021 (Journal Indexed in SCI) identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 3-4
  • Publication Date: 2021
  • Title of Journal : JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING
  • Page Numbers: pp.277-294
  • Keywords: Decision support systems, hierarchical decision making, intuitionistic BWM, intuitionistic fuzzy sets, multi-criteria decision making, sustainable transportation service provider, DECISION-MAKING, OPERATIONS, SWARA, SETS, QFD

Abstract

Today, transportation systems are seen as a significant component of people's daily routines. It was stated that nearly forty percent of the world's population passes approximately one hour out each day. Sustainable transportation systems aims to contribute economy and diminish environmental affects by ensuring various benefits as effective city management, energy efficiency, road safety and decreased fuel consumption. Also, sustainable transportation has a key status in logistics and supply chain management. It constructs the principals of sustainable supply chain management. Identification of the most appropriate sustainable transportation service provider needs to consider various factors that are yielded in a hierarchical structure. In this paper, best-worst method (BWM) integrated hierarchical distance based intuitionistic decision making approach to valuate transportation service providers regarding the sustainability factors from social, environmental, economic and operational perspectives. The developed framework manages vague and uncertain data and it enables to present hesitation via intuitionistic fuzzy numbers. Intuitionistic BWM, which do not require to make a pairwise comparisons among criteria is utilized to determine the criteria weights. Moreover, the hierarchical representation of the criteria ensures to conduct an effective analysis.