Application of Type-2 Fuzzy Logic to a Multiobjective Green Solid Transportation-Location Problem With Dwell Time Under Carbon Tax, Cap, and Offset Policy: Fuzzy Versus Nonfuzzy Techniques

Das S. K., Roy S. K., Weber G.

IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol.28, no.11, pp.2711-2725, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 28 Issue: 11
  • Publication Date: 2020
  • Doi Number: 10.1109/tfuzz.2020.3011745
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.2711-2725
  • Keywords: Carbon tax, cap, offset policy, facility location problem (FLP), fuzzy multiobjective optimization, solid transportation problem (STP), type-2 intuitionistic fuzzy set (T2IFS), SUPPLY CHAIN NETWORK, FACILITY LOCATION, CLIMATE-CHANGE, GAS EMISSIONS, OPTIMIZATION, MANAGEMENT, MODEL, MULTIPERIOD, HEALTH
  • Middle East Technical University Affiliated: No


We are observing more often extreme climate incidents because of global warming. There is a dire requirement for governments, enterprises, the overall population, and academics to take facilitated activities so as to handle the difficulties forced by environmental change. The most important strategic issue is to design an effective and environmentally concerned logistics system as transportation is one of the fundamental reasons for carbon emanations. Having this goal and mentioned highly important contributions in the field, this article introduces an unprecedented integrated mathematical model for a green solid transportation system with dwell time to execute the carbon tax, cap, and offset regulation. Due to market fluctuations, the supply and demand parameters are not always of crisp nature. Hence, a twofold (type-2 intuitionistic) uncertainty is incorporated in this article to provide a realistic transportation system. A new ranking defuzzification technique is presented for conversion into a deterministic form. After that, a fuzzy technique and a nonfuzzy technique are used to get the Pareto-optimal solution of the proposed problem. The performances of our findings are discussed with industrial-based application examples. Moreover, a comparative study is explored among the other relevant existing techniques. Managerial insights, conclusions, and avenues of future scopes are offered at the end of this article.