In this Chapter, we imagine a decision maker (or a group of experts) trying to establish or examine fair procedures to combine opinions about alternatives related to different points of view.

Multiple criteria decision making

Industry 4.0 technologies assessment: A sustainability perspective

Spherical fuzzy sets and spherical fuzzy TOPSIS method

The disasters caused by epidemic outbreaks is different from other disasters due to two specific features: their long-term disruption and their increasing propagation. Not controlling such disasters brings about severe disruptions in the supply chains and communities and, thereby, irreparable losses will come into play. Coronavirus disease 2019 (COVID-19) is one of these disasters that has caused severe disruptions across the world and in many supply chains, particularly in the healthcare supply chain. Therefore, this paper, for the first time, develops a practical decision support system based on physicians' knowledge and fuzzy inference system (FIS) in order to help with the demand management in the healthcare supply chain, to reduce stress in the community, to break down the COVID-19 propagation chain, and, generally, to mitigate the epidemic outbreaks for healthcare supply chain disruptions. This approach first divides community residents into four groups based on the risk level of their immune system (namely, very sensitive, sensitive, slightly sensitive, and normal) and by two indicators of age and pre-existing diseases (such as diabetes, heart problems, or high blood pressure). Then, these individuals are classified and are required to observe the regulations of their class. Finally, the efficiency of the proposed approach was measured in the real world using the information from four users and the results showed the effectiveness and accuracy of the proposed approach.

A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19).

The extensions of ordinary fuzzy sets such as intuitionistic fuzzy sets, Pythagorean fuzzy sets, and neutrosophic sets, whose membership functions are based on three dimensions, aim at collecting experts’ judgments more informatively and explicitly. In the literature, generalized three-dimensional spherical fuzzy sets have been introduced by Kutlu Gundogdu and Kahraman (J Intell Fuzzy Syst 36(1):337–352, 2019a), including their arithmetic operations, aggregation operators, and defuzzification operations. In this paper, our aim is to extend classical analytic hierarchy process (AHP) to spherical fuzzy AHP (SF-AHP) method and to show its applicability and validity through a renewable energy location selection example and a comparative analysis between neutrosophic AHP and SF-AHP.

A novel spherical fuzzy analytic hierarchy process and its renewable energy application

A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system

A new risk assessment approach: Safety and Critical Effect Analysis (SCEA) and its extension with Pythagorean fuzzy sets

Evaluation based on distance from average solution (EDAS) method is based on the distances of each alternative from the average solution with respect to each criterion. This method is similar to distance-based multi-criteria decision-making methods such as TOPSIS and VIKOR. It simplifies the calculation of distances to the deal solution and determines the final decision rapidly. EDAS method has been already extended to its ordinary fuzzy, intuitionistic fuzzy and type-2 fuzzy versions. In this paper, we extend EDAS method to its interval-valued neutrosophic version with the advantage of considering a expert's truthiness, falsity, and indeterminacy simultaneously. The proposed method has been applied to the prioritization of United Nations national sustainable development goals, and one-at-a-time sensitivity analysis is conducted to check the robustness of the given decisions. The proposed method is also compared with the intuitionistic fuzzy TOPSIS method for its validity.

A novel interval-valued neutrosophic EDAS method: prioritization of the United Nations national sustainable development goals

Multi-criteria decision-making (MCDM) methods are susceptible to the subjectivity of experts when especially they use linguistic terms for assessment. This subjectivity and vagueness in the evaluation process have been handled by the recent extensions of ordinary fuzzy sets such as type-2 fuzzy sets, hesitant fuzzy sets, intuitionistic fuzzy sets, Pythagorean fuzzy sets and neutrosophic sets. Pythagorean fuzzy sets are superior to the other extensions with a more flexible definition of membership function. A novel Pythagorean fuzzy AHP method has been developed for MCDM. The developed method has been applied to a landfill site selection problem for the city of Istanbul in Turkey. The proposed method has successfully evaluated the landfill location alternatives with respect to the considered criteria. The results are compared with ordinary fuzzy AHP, and it is revealed that the proposed method produces consistent and informative outcomes better representing the uncertainty of decision-making environment. Robustness of the decision given by the proposed method is ensured by conducting one-at-a-time sensitivity analysis.

Ali Karaşan

Papers

A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system

A new risk assessment approach: Safety and Critical Effect Analysis (SCEA) and its extension with Pythagorean fuzzy sets

A novel interval-valued neutrosophic EDAS method: prioritization of the United Nations national sustainable development goals

A novel pythagorean fuzzy AHP and its application to landfill site selection problem

A novel intuitionistic fuzzy DEMATEL – ANP – TOPSIS integrated methodology for freight village location selection