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Sustainability in transportation is vital for lessening environmental effects and ensuring the long-term viability of the transportation industry. The aviation sector, a significant player in sustainable transport, has encountered obstacles in reducing its ecological footprint, especially in aircraft design and operation. Symmetry and asymmetry exist widely in the aviation sector, as in other industrial applications. Multicriteria decision making (MCDM) is an important research topic that is widely applied to practical decision-making problems and is considered a complex tool with which to balance the symmetry between goals and conflicting criteria. This research assesses different airplane models in order to pinpoint the most environmentally friendly options using the Bayesian best–worst (BWM) method for evaluating criteria and the consensus-based intuitionistic fuzzy evaluation based on distance from average solution (IF EDAS) method for assessing alternatives. The Bayesian BWM approach facilitates group decision making on criteria evaluation and systematically evaluates factors like fuel efficiency, emissions, and noise levels, offering a holistic view of each airplane model's sustainability performance. Conversely, consensus-based IF EDAS allows for the ranking of alternatives based on their distance to the average solution for a sustainable airplane model while concurrently considering multiple criteria, guiding decisionmakers to a consensus and eliminating the asymmetry between opinions before the final decision. This study reveals that technical features are the critical criteria for airplanes and significantly influence alternative rankings. The findings indicate that the chosen decision criteria greatly impact the selection of the most sustainable airplane model as part of a sustainable transportation system, potentially altering ranking orders. [ABSTRACT FROM AUTHOR]

Recently, with the development in technology and increase in population, the waste generated becomes an important issue for countries. Some regulations of recently updated through Ministry of Environment and Urbanisation in Turkey; it is essential to use environmentally compatible physical, chemical, or biological technologies to ensure the recovery of municipal waste in accordance with the zero-waste management system. Currently, there is one pre-treatment facility at the Anatolian Side of Istanbul and a pre-treatment facility is needed to use waste recycling facilities more efficiently. In this research, a combined fuzzy multi-criteria decision-making (MCDM) methodology is developed for the mechanical biological treatment (MBT) location selection problem. The proposed method comprises of geographic information system (GIS), fuzzy best worst method (BWM), and hierarchical fuzzy MCDM approach. First, alternatives for the site selection problem were determined using expert opinions and web-based GIS. Second, the criteria determined for the literature and expert opinions were weighted using the fuzzy best worst method. Finally, most suitable alternative was determined using hierarchical fuzzy MCDM approach. This study creates a roadmap for the MBT facility needed in the Anatolian Side of Istanbul, and according to the literature survey, there is no other study conducted on this subject. The proposed method handles vagueness and uncertainty employing fuzzy set theory. Fuzzy BWM, which does not need to conduct pairwise comparison among all criteria, is employed to determine the criteria weights. Web-based GIS that presents spatial data on web environment offers ease of use and provides the opportunity to evaluate over ready data. [ABSTRACT FROM AUTHOR]

Research and development (R&D) activities are essential to guarantee continuity of firms, meet customer requirements and keep ahead in competition. R&D project selection constitutes a significant part of project management in order to achieve the desired results and outputs. In this study, an integrated fuzzy multi-criteria group decision making approach is developed for R&D project selection. The problem includes a hierarchical structure of the criteria, uncertainty in evaluating the relative importance of criteria/sub-criteria and rating of candidate projects. The method employs the ordered weighted average (OWA) operator as the aggregation operator, which helps to fully reflect the real behavior of the decision makers in group decision making problems. Fuzzy integral method, which does not require the assumption of the mutual independence of criteria, is used to rank the alternatives. The case study is conducted in a small-sized company in Turkey, which designs and produces special purpose machines. A R&D project selection model is developed to maximize the desired outputs. The results of the analysis show that technological, environmental, marketing, organizational, national and financial issues should be considered simultaneously in the evaluation process. The proposed method is shown to be efficient, generalizable and practical and it has several significant merits compared to the other methods. [ABSTRACT FROM AUTHOR]

United Nations Development Program presented the Human Development Index for ranking the countries with regard to three dimensions, namely being knowledgeable, a long and healthy life, and having a proper standard of living. In order to provide social, economic, and environmental improvement for the countries, 2030 Agenda for Sustainable Development was presented with seventeen sustainable goals. This study proposes a fuzzy multiple criteria group decision making procedure combining quality function deployment and data envelopment analysis (DEA). The developed approach enables to include the interactions among country evaluation criteria via forming a house of quality (HOQ). The lower and upper bounds on country evaluation criteria are determined by utilizing fuzzy weighted average (FWA) technique. A common-weight DEA-based modeling framework, which uses the weights of country attributes calculated by FWA with the data from HOQ, is employed for identifying country ratings with respect to related country attributes. The proposed approach is implemented for ranking Latin American countries in a way to incorporate imprecise data and inner dependence among evaluation criteria. According to the results, Peru ranks first, followed by Chile and Costa Rica. Moreover, rankings obtained from human development index and the proposed formulation are compared via Spearman's rank correlation. While the obtained rankings of countries according to the proposed approach are positively correlated with those of human development index, the variations in rankings may be due to considering a wider coverage of sustainable development goals as well as interactions among them by the proposed methodology. [ABSTRACT FROM AUTHOR]

Supply chain agility is an indispensable way for the companies to quickly response to the demands of the customers. For this reason, agility of supply chain is indispensable in dynamic markets that have high scale of diversity and subjective needs. Supply chain agility needs a systemic procedure that gives priority to feedbacks of customers and follows the changes of competitors in the sector. An efficient supplier evaluation procedure is indispensable for reaching supply chain agility. Agile supplier selection needs to take into account various criteria that incorporate vagueness and uncertainty, obtaining general a multiple level hierarchical system that allows conducting a more efficient decision analysis. Thus, in this paper an integrated fuzzy multi-criteria group decision making procedure based on quantifier-guided ordered weighted average (OWA) method and fuzzy integral, which allows incorporating uncertain data expressed as linguistic terms into the analysis, is proposed for identifying the most suitable agile supplier alternative. In group decision making issues, aggregating experts'opinions is vital to achieve more robust results. As quantifier-guided OWA method is appropriate for decision making problems under uncertain environments, it is employed for the aggregation of experts'evaluations. The developed decision procedure is illustrated via a case study performed in a dye producer in Turkish dye sector. [ABSTRACT FROM AUTHOR]

Today, organizations give more priority to new product development (NPD) processes in order to gain competitive advantages and to satisfy customers' requirements. Identification of a suitable material is a key issue in the design and development of new products. Material selection can be viewed as a complex multi-criteria decision making (MCDM) problem that needs to consider multiple conflicting criteria. The aim of this paper is to develop a fuzzy multi-criteria group decision making methodology, which combines 2-tuple fuzzy linguistic representation model, linguistic hierarchies, quality function deployment (QFD), and fuzzy TOPSIS method to specify the importance of selection attributes in material selection procedure and to determine the most appropriate material alternative that best fits the customers' requirements. The developed approach is illustrated through a case study of washing liquid material selection procedure. [ABSTRACT FROM AUTHOR]

Abstract: Supplier selection is a highly important multi-criteria group decision making problem, which requires a trade-off between multiple criteria exhibiting vagueness and imprecision with the involvement of a group of experts. In this paper, a fuzzy multi-criteria group decision making approach that makes use of the quality function deployment (QFD) concept is developed for supplier selection process. The proposed methodology initially identifies the features that the purchased product should possess in order to satisfy the company’s needs, and then it seeks to establish the relevant supplier assessment criteria. Moreover, the proposed algorithm enables to consider the impacts of inner dependence among supplier assessment criteria. The upper and the lower bounds of the weights of supplier assessment criteria and ratings of suppliers are computed by using the fuzzy weighted average (FWA) method. The FWA method allows for the fusion of imprecise and subjective information expressed as linguistic variables or fuzzy numbers. The method produces less imprecise and more realistic overall desirability levels, and thus it rectifies the problem of loss of information. A fuzzy number ranking method that is based on area measurement is used to obtain the final ranking of suppliers. The computational procedure of the proposed framework is illustrated through a supplier selection problem reported in an earlier study. [Copyright &y& Elsevier]

Abstract: Health-care waste (HCW) management is a high priority environmental, public and health concern in developing countries. The management and treatment of HCW are gaining more attention with the rising awareness. The objective of this research is to propose multi-criteria decision making techniques for conducting an analysis based on multi-level hierarchical structure and fuzzy logic for the evaluation of HCW treatment alternatives. The ideal and anti-ideal solutions are taken into consideration simultaneously in the developed approaches to account for the individuals’ desire to be both as close as possible to the ideal and as distant as possible from the anti-ideal. The proposed decision approaches enable the decision-makers to use linguistic terms, and thus, reduce their cognitive burden in the evaluation process. Furthermore, a hierarchy of evaluation criteria and their related sub-criteria is employed in the presented approaches in order to conduct a more effective analysis. The computational procedures of the proposed frameworks are illustrated through a case study in Istanbul, the largest city of Turkey that is also listed among the world''s most crowded cities. The HCW treatment alternatives considered in this study include “incineration”, “steam sterilization”, “microwave” and “landfill”. The results obtained using the proposed decision approaches are analyzed in a comparative way. [Copyright &y& Elsevier]

Abstract: Nowadays, as in all other organizations, the amount of waste generated in the health-care institutions is rising due to their extent of service. Medical waste management is a common problem of developing countries including Turkey, which are becoming increasingly conscious that health-care wastes require special treatment. Accordingly, one of the most important problems encountered in Istanbul, the most crowded metropolis of Turkey, is the disposal of health-care waste (HCW) from health-care institutions. Evaluating HCW disposal alternatives, which considers the need to trade-off multiple conflicting criteria with the involvement of a group of experts, is a highly important multi-criteria group decision making problem. The inherent imprecision and vagueness in criteria values concerning HCW disposal alternatives justify the use of fuzzy set theory. This paper presents a fuzzy multi-criteria group decision making framework based on the principles of fuzzy measure and fuzzy integral for evaluating HCW treatment alternatives for Istanbul. In group decision making problems, aggregation of expert opinions is essential for properly conducting the evaluation process. In this study, the ordered weighted averaging (OWA) operator is used to aggregate decision makers’ opinions. Economic, technical, environmental and social criteria and their related sub-criteria are employed to assess HCW treatment alternatives, namely “incineration”, “steam sterilization”, “microwave”, and “landfill”. A comparative analysis is presented using another classical operator to aggregate decision makers’ preferences. [Copyright &y& Elsevier]

Abstract: Many individual attributes considered for personnel selection such as organizing ability, creativity, personality, and leadership exhibit vagueness and imprecision. The fuzzy set theory appears as an essential tool to provide a decision framework that incorporates imprecise judgments inherent in the personnel selection process. In this paper, a fuzzy multi-criteria decision making (MCDM) algorithm using the principles of fusion of fuzzy information, 2-tuple linguistic representation model, and technique for order preference by similarity to ideal solution (TOPSIS) is developed. The proposed method is apt to manage information assessed using both linguistic and numerical scales in a decision making problem with multiple information sources. Furthermore, it enables managers to deal with heterogeneous information. The decision making framework presented in this paper employs ordered weighted averaging (OWA) operator that encompasses several operators as the aggregation operator since it can implement different aggregation rules by changing the order weights. The aggregation process is based on the unification of information by means of fuzzy sets on a basic linguistic term set (BLTS). Then, the unified information is transformed into linguistic 2-tuples in a way to rectify the problem of loss information of other fuzzy linguistic approaches. The computational procedure of the proposed framework is illustrated through a personnel selection problem reported in an earlier study. [Copyright &y& Elsevier]

The new product development process (NPD) is considered to be the key factor of competition among different markets. The identification of a suitable material is an important issue in the conception and improvement of new products. Material selection is seen as an important multi-criteria decision making (MCDM) problem in engineering because of the requirement of considering multiple criteria from different dimensions. Improper material selection may negatively affect the success of a firm. The purpose of this study is to specify the importance of selection attributes, which are considered to evaluate washing liquid that meets the needs of both customers and firms. Then, it objects to choose the most appropriate alternative among various formulations. A fuzzy MCDM methodology based on quality function deployment (QFD), 2-tuple fuzzy linguistic representation, and linguistic hierarchies is presented. QFD is used to incorporate customer requirements into the evaluation process. The 2-tuple fuzzy modeling and linguistic hierarchies are employed to combine multi-granular data given by experts. Finally, the fuzzy COPRAS (Complex Proportional Assessment) method is used to choose the most suitable alternative. The implementation of the developed method is presented by a case study conducted on a detergent manufacturer located in Turkey. [ABSTRACT FROM AUTHOR]