Generalized trapezoidal fuzzy soft set: application to green supplier selection problem

Üretim faaliyetlerinin aksamaması, ürün ya da hizmet kalitesinin geliştirilebilmesi ve ürünlerin toplam maliyetlerinin kontrol altında tutulabilmesi için tedarikçilerin değerlendirilmesi ve seçimi organizasyonlar açısından stratejik kararlar arasında yer almaktadır. Son yıllarda çevrenin korunmasına yönelik olarak yapılan yasal düzenlemeler artmış ve müşteriler çevre konusunda daha bilinçli hale gelmiştir. Bu nedenle ürünlerin çevreye olan zararlı etkilerini azaltmak için ürünlerin üretiminde kullanılan hammaddenin temininden, ürünün müşteriye teslim edilmesine kadar geçen tüm süreçlerde organizasyonlar faaliyetlerinin tamamını çevreye duyarlı hale getirmek zorundadır. Tedarikçi seçimi ise bu faaliyetlerin başlangıç noktasını oluşturmaktadır. Tedarikçi seçimi probleminde alternatiflerin değerlendirilebilmesi ve seçimi için kalitatif ve kantitatif çok sayıda kriter dikkate alınmaktadır. Kalitatif kriterlere göre alternatiflerin değerlendirilmesi genellikle dilsel ifadeler kullanılarak yapılmaktadır ve bulanık sayılara dönüştürülmektedir. Bu nedenle bu problemlerin çözümünde genellikle bulanık ya da bulanık olmayan çok kriterli karar verme yöntemleri kullanılmaktadır. Genelleştirilmiş trapezoidal bulanık esnek kümeler özellikle bulanık ortamda karar vermeyi kolaylaştıran tekniklerdir. Trapezoidal bulanık esnek kümeler ile kesin olmayan ya da belirsiz bilgiler etkili bir şekilde dilsel değişkenler olarak tanımlanabilmektedir ve bu nedenle çok kriterli karar verme yöntemlerine alternatif olarak kullanılabileceği düşünülmektedir. Bu amaçla çalışmada, genelleştirilmiş trapezoidal bulanık esnek küme yaklaşımı yeşil tedarikçi seçimi problemine uygulanarak, elde edilen sonuçlar analiz edilmiştir. Çalışmanın sonucunda genelleştirilmiş trapezoidal bulanık esnek kümelerin çok kriterli karar verme yaklaşımlarının yerine kullanılabileceği görülmüştür.Evaluation and selection of suppliers are among the strategic decisions for organizations in order to not to interrupt production activities, improve product or service quality and keep the total cost of products under control. In recent years, the legal regulations for environmental protection have increased and customers have become more conscious about the environment. Therefore, in order to reduce the harmful effects of the products to the environment, organizations must make all their activities sensitive to the environment in all the processes from the procurement of the raw materials used in the production of the products to distribution of products to customers. Supplier selection is the starting point of these activities. Numerous qualitative and quantitative criteria are considered for the selection and evaluation of alternatives in the supplier selection problem. Evaluation of alternatives according to qualitative criteria is usually done using linguistic expressions and converted to fuzzy numbers. Therefore, fuzzy or non-fuzzy multi-criteria decision making methods are generally used to solve these problems. Generalized trapezoidal fuzzy soft set are techniques that facilitate decision making, especially in fuzzy environments. With trapezoidal fuzzy soft sets, uncertain or ambiguous information can be effectively defined as linguistic variables and is therefore considered to be an alternative to multi-criteria decision-making. In this study, generalized trapezoidal fuzzy flexible cluster approach was applied to green supplier selection problem and the results were analyzed. As a result of the study, it is seen that generalized trapezoidal fuzzy flexible clusters can be used instead of multi criteria decision making approaches

A Fuzzy Based Decision Making Approach for Selecting and Evaluating Green Suppliers

In a competitive business environment, green supplier selection approach plays a pivotal role in supply chain management, because, due to growing global concern of environmental protection, green production has become an important factor for almost every manufacturer and will influence the sustainability of a manufacturer in the long run. A performance evaluation system for green suppliers is therefore required to determine the suitability of suppliers to cooperate with the industry. Supplier selection is basically depends on decision makers’ (experts’) assessments. This process inevitably involves various types of uncertainties such as deception, fuzziness and incompleteness due to the shortcomings of the human being’s subjective judgment and it’s variance from one human being to another. However, the existing methods cannot properly integrate uncertainties into the determination of green suppliers and their selection. Nowadays, many companies have begun to implement green supply chain management and to consider environmental issues and the measurement of their suppliers’ environmental performance. Here we have adopted, an effective method for selecting and evaluating green supplier selection; TOPSIS (Technique for order preference by similarity to Ideal Solution

Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories

The present work articulates few case empirical studies on decision making in industrial context. Development of variety of Decision Support System (DSS) under uncertainty and vague information is attempted herein. The study emphases on five important decision making domains where effective decision making may surely enhance overall performance of the organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply chain’s g-resilient index and v) risk assessment in e-commerce exercises. Firstly, decision support systems in relation to robot selection are conceptualized through adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey set theory is also found useful in this regard; and is combined with TODIM approach to identify the best robot alternative. In this work, an attempt is also made to tackle subjective (qualitative) and objective (quantitative) evaluation information simultaneously, towards effective decision making. Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a novel decision support framework is proposed to address g-resilient (green and resilient) supplier selection issues. Green capability of suppliers’ ensures the pollution free operation; while, resiliency deals with unexpected system disruptions. A comparative analysis of the results is also carried out by applying well-known decision making approaches like Fuzzy- TOPSIS and Fuzzy-VIKOR. In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance- Based’ model in combination with grey set theory to deal with 3PL provider selection, considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is articulated to demonstrate application potential of the proposed model. The results, obtained thereof, have been compared to that of grey-TOPSIS approach. Another part of this dissertation is to provide an integrated framework in order to assess gresilient (ecosilient) performance of the supply chain of a case automotive company. The overall g-resilient supply chain performance is determined by computing a unique ecosilient (g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient criteria in accordance to their current status of performance. The study is further extended to analyze, and thereby, to mitigate various risk factors (risk sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are recognized and evaluated in a decision making perspective by utilizing the knowledge acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying parameters are assessed in a subjective manner (linguistic human judgment), rather than exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem context (toward e-commerce success). Risks are now categorized into different levels of severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic). Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect the company’s e-commerce performance are recognized through such categorization. The overall risk extent is determined by aggregating individual risks (under ‘critical’ level of severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then used to obtain structural relationship amongst aforementioned five risk sources. An appropriate action requirement plan is also suggested, to control and minimize risks associated with e-commerce exercises

Large-Scale Green Supplier Selection Approach under a Q-Rung Interval-Valued Orthopair Fuzzy Environment

As enterprises pay more and more attention to environmental issues, the green supply chain management (GSCM) mode has been extensively utilized to guarantee profit and sustainable development. Greensupplierselection(GSS),whichisakeysegmentofGSCM,hasbeeninvestigated to put forward plenty of GSS approaches

A decision support tool for sustainable supplier selection in manufacturing firms

Purpose: Most original equipment manufacturers (OEMs) are strategically involved in supplier base rationalization and increased consciousness of sustainable development thus, reinforcing need for accurately considering sustainability in supplier selection to improve organizational performance. In real industrial case, imprecise data, ambiguity of human judgment, uncertainty among sustainability factors and the need to capture all subjective and objective criteria are unavoidable and pose huge challenge to accurately incorporate sustainability factors into supplier selection. Methodology: This study develops a model based on integrated multi- criteria decision making (MCDM) methods to solve such problems. The developed model applies Fuzzy logic, DEMATEL and TOPSIS to effectively analyze the interdependencies between sustainability criteria and to select the best sustainable supplier in fuzzy environment while capturing all subjective and objective criteria. A case study is illustrated to test the proposed model in a gear manufacturing company, an OEM to provide insights and for practical applications. Findings: Results show that social factors of sustainability ranks as the most important in supplier selection. However, the most influential sustainability sub- criteria are work safety (WS) and quality. Originality/Value: The model is capable of capturing all subjective and objective criteria in fuzzy environment to accurately incorporate sustainability factors in supplier selection. It is decision support tool relevant for providing insights to managers while implementing sustainable supplier selection.Peer Reviewe