50 years of fuzzy set theory and models for supplier assessment and selection a literature review

Accepted Manuscript Years of fuzzy set theory and models for supplier assessment and selection: A literature review Dragan Simi´c, Ilija Kovaˇcevi´c, Vasa Svirˇcevi´c, Svetlana Simi´c PII: DOI: Reference: S1570-8683(16)30070-2 http://dx.doi.org/10.1016/j.jal.2016.11.016 JAL 447 To appear in: Journal of Applied Logic Please cite this article in press as: D Simi´c et al., Years of fuzzy set theory and models for supplier assessment and selection: A literature review, J Appl Log (2016), http://dx.doi.org/10.1016/j.jal.2016.11.016 This is a PDF file of an unedited manuscript that has been accepted for publication As a service to our customers we are providing this early version of the manuscript The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain 50 Years of Fuzzy Set Theory and Models for Supplier Assessment and Selection: a Literature Review Dragan Simiỹ1*, Ilija Kovaỵeviỹ1, Vasa Svirỵeviỹ2 and Svetlana Simiü3 1University of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradoviüa 6, 21000 Novi Sad, Serbia dsimic@eunet.rs, ilijak@uns.ac.rs 2Lames Ltd., Jaraỵki put bb., 22000 Sremska Mitrovica, Serbia vasasv@hotmail.com 3University of Novi Sad, Faculty of Medicine, Hajduk Veljkova 1–9, 21000 Novi Sad, Serbia drdragansimic@gmail.com Abstract Supplier assessment and selection mapping as an essential component of supply chain management are usually multi-criteria decision-making problems Decision making is the thought process of selecting a logical choice from the available options This is generally made under fuzzy environment Fuzzy decision-making is a decision process using the sets whose boundaries are not sharply defined The aim of this paper is to show how fuzzy set theory, fuzzy decision-making and hybrid solutions based on fuzzy can be used in the various models for supplier assessment and selection in a 50 year period Keywords Fuzzy set theory, supplier assessment, supplier selection, fuzzy logic, uncertainty, logistics, supply chain Introduction Supply chain management and strategic sourcing are among the fastest growing areas of management Most companies in production and manufacturing industries are seeking the most appropriate supplier to improve economic efficiency Phenomenon of globalization and rapid development of logistics, at the same time, is in details presented in [1] Enterprises have recently become more dependent on suppliers, and direct and indirect consequences of poor decision-making become more severe Supplier selection is an important aspect of competition and it determines the fate of an enterprise Fifty years ago, in 1965, Zadeh introduced fuzzy set theory to cope with the imprecision and uncertainty which is inherent to human judgment in decision making processes through the use of linguistic terms and degrees of membership A fuzzy set is a class of objects with grades of membership These grades present the degree of stability to which certain element belongs to a fuzzy set [2] Therefore, it is economically sensible for an enterprise decision maker to use fuzzy set theory, one of the artificial intelligence (AI) techniques, which have limited use in this research This paper continues author’s year research in supplier assessment, ranking and selection domain, which is presented in [3-5] The aim of this paper is to show how fuzzy set theory, fuzzy decision-making and hybrid solutions and synergy based on fuzzy can be used in various supplier assessment and selection models during a 50 year period This paper outlines some long time approaches of fuzzy models which are implemented in the terms of potential benefits gained in supplier assessment and selection in order to mitigate the uncertainty and risks of the global world business turbulent environment The rest of the paper is organized in the following way: Section overviews early period of Fuzzy Set Theory, business influence on supplier assessment and selection of related work Section shows Existing analytical methods for supplier assessment and selection Section presents Fuzzy Models in Supplier Assessment and Selection in two sub-sections: the first one being individual fuzzy approaches; and the second integrated fuzzy approaches, while Section gives concluding remarks Related Work - Fuzzy Set Theory and Supplier Assessment Related work could be discussed from two deferent points of view The first point of view deals with the roots and early researches on fuzzy set theory (FST) and the second point of view deals with deferent influences technology and rapid development have on supplier assessment and selection 2.1 Early Fuzzy Set Lotfali Askar Zadeh introduced fuzzy sets and systems for the first time in 1965 in a well known paper [2], in Information and Control journal and a chapter [6] in book called System Theory But, before him, Max Black was the first one to introduce a very similar idea in 1937: Vagueness An Exercise in Logical Analysis, was a chapter title in book Philosophy of Science [7] Almost the same idea was mentioned in 1952, when Stephen Cole Kleene published his book Introduction to Metamathematics [8] The same idea appeared in Abraham Robinson’s book Introduction to Model Theory and to the Metamathematics of Algebra, published in 1963 [9] But, Lotfi A Zadeh was the one who completed all of the previous researches in 1965, and since then fuzzy set theory has presented an inexhaustible research subject for numerous researchers in the world 2.2 Business Influence on Supplier Assessment and Selection Nowadays, costs of purchasing raw materials and component parts from external suppliers are very important As an example, in automotive industry, costs of components and parts purchased from external sources may in total make up more than 50 times the costs for high-technology firms [10] The search for new suppliers is a continuous priority for any company in order to upgrade the variety and typology of their production range [11] There are two key reasons for this The main, general, reason is that product life cycle is very short, from to years, and new models must often be developed using completely renewed materials or new technologies And the second reason is that the industries are, historically, labor intensive sectors Current technologies and organizational forms require involvement of more decision-makers The influence of these developments on the complexity and importance of purchasing decisions is shown in Fig [12] In addition, several developments further complicate purchasing decision-making Changing customer preferences, public-government procurement regulations, increase in outsourcing, globalization of trade and the Internet enlargement are all changing a purchaser’s choice set [13] Fig Impact of developments on the complexity of initial purchasing decisions [12] Global competition, mass customization, high customer expectations and harsh economic conditions are forcing companies to rely on external suppliers to contribute a larger portion of parts, materials, and assemblies to finished products and to manage a growing number of processes and functions that were once controlled internally Therefore, supplier categorization, selection and performance evaluation are of strategic importance to companies Analytical Methods for Supplier Assessment and Selection Supplier assessment and selection decisions are complicated by the fact that various criteria must be considered in a decision making process Many scientists and practitioners since the 1960’s have been focused on the analysis of criteria for selecting and measuring supplier performance An interesting work, which is a reference for majority of papers dealing with supplier or vendor selection problem, was presented by Gary W Dickson [14] He defined 23 criteria for supplier selection, with regard to their importance At that time (1966), 50 years ago, the most significant criteria were the ”quality” of the product, the ”on-time delivery”, the ”performance history” of the supplier and the ”warranty policy” used by supplier It is important to mention that with pronounced emphasis on manufacturing and organizational philosophies such as Just-in-Time (JIT) and Total Quality Management (TQM), and the growing importance of supply chain management concepts, the need for considering supplier relationships from a strategic perspective has become even more apparent [15] With the recent emphasis on supply chain management, strategic sourcing becomes even more important to improve company’s performance [16] Purchasers always consider multi-criteria approach when selecting suppliers [10] Numerous multiple-criteria decision-making (MCDM) techniques, ranging from simple weighted averaging to complex mathematical programming models have been applied to solve supplier evaluation and selection problems Fig Existing analytical methods for supplier assessment and selection [18] According to [17], data envelopment analysis (DEA) is the most often used MCDM approach (30%), followed, in order of distribution, by mathematical programming (17%), analytical hierarchy processes (AHP) (15%), case-based reasoning (CBR) (11%), fuzzy set theory (10%) and analytical network processes (ANP) (5%) Summarized, Existing analytical methods for decision models in supplier assessment and selection, are based on: 1) Single models: (a) Mathematics, (b) Statistics, and (c) Artificial Intelligence, 2) Combined models: (a) AHP, (b) DEA; and is illustrated in Fig [18] And, although this model [18] is from 2011 and only years old, the entire section of Combined models should be appropriately expanded, not just presented with the AHP and DEA hybrid models 4 Fuzzy Models in Supplier Assessment and Selection Supplier assessment and selection are usually multi-criteria decision problems which, in actual business contexts, may have to be solved in the absence of precise information In order to this, the decision process of purchasing could be modeled and structured in a realistic way A number of authors suggest using a fuzzy sets theory (FST) to model uncertainty and imprecision in supplier choice situations In short, FST offers a mathematically precise way of modeling vague preferences, for example setting weights of performance scores on criteria Simply stated, FST makes it possible to mathematically describe statements like: ”criterion X should have a weight of around 0.8” FST can be combined with other techniques to improve the quality of the final tools [13] SUPPLIER SELECTION METHODS INDIVIDUAL APPROACHES MATHEMATICS STATISTICAL MODEL INTEGRATED APPROACHES ARTIFICIAL INTELLIGENCE FUZZY SET THEORY (FST) ANALYTIC HIERARCHY PROCESS (AHP) Analytic Hierarchy Process (AHP) Cluster Analysis Fuzzy Set Theory (FST) FST + MCDM AHP + GP Linear Programming (LP) Multiple Regression Neural Networks (NN) FST + Mathematics AHP + LP Multi-Objective Programming (MOP) Discriminant Analysis Genetic Algorithm (GA) FST + Statistics Total Cost Ownership (TCO) Conjoint Analysis Case-Based Reasoning (CBR) FST + AI Goal Programming (GP) Principal Component Analysis (PCA) Software Agent (SA) Data Envelopment Analysis (DEA) DATA ENVELOPMENT ANALYSIS (DEA) DEA + MOP Expert Systems (ES) Fig The proposed model – Methods for supplier assessment and selection, individual and integrated approaches (extended by authors based on [18]) The developed and proposed model for – Methods for supplier assessment and selection – is presented in Fig It could be divided in two major approach groups The first one being a group of Individual fuzzy approaches and the second one, group of Integrated fuzzy approaches, similar to Existing analytical methods where there are Single and Combined models, as presented in Fig Individual fuzzy approaches are models where only fuzzy logic and fuzzy set theory are implemented to solve realworld problems On the other hand, Integrated fuzzy approaches combine fuzzy set theory with numerous: multiple-criteria decision-making (FST + MCDM); mathematics (FST + Mathematics); statistics (FST + Statistics); artificial intelligence (FST + AI); models and techniques Red text in grey fill boxes, in Fig 3., presents our extensions of the original model These extensions are in great detail discussed further in this paper Blue text in white fill boxes also presents our extensions but they are not discussed in this paper 4.1 Review of Individual and Integrated Fuzzy Approaches In this research, the authors have, meticulously, collected papers dealing with: (1) supplier assessment, (2) supplier evaluation, and (3) supplier selection The authors collected a very large body of papers from journal and conference proceedings For this review, the authors selected only 54 papers published in respectable journals Integrated fuzzy approaches Individual Table Review of individual and integrated fuzzy approaches Methods Fuzzy linguistic quantifier Numerical and linguistic information Fuzzy strategic system Fuzzy AHP Fuzzy ANP Fuzzy MADM Integrated Fuzzy QFD Fuzzy Fuzzy TOPSIS MCDM Fuzzy PROMETHEE Approaches Fuzzy VIKOR Fuzzy SMART Fuzzy SWOT Fuzzy DEA Fuzzy linear programming (LP) Integrated Fuzzy goal programming (GP) Fuzzy MP Fuzzy MOP Approaches Fuzzy MOM Fuzzy Integral model Integrated Fuzzy CA Fuzzy Fuzzy probability assignments Statistical Approaches Fuzzy GA Integrated Fuzzy inference system Fuzzy AI Adaptive neuro-fuzzy IS Approaches Fuzzy neural network References [19] [21] [23] [25] [31] [35] [39] [41] [46] [47] [48] [50] [51] [52] [54] [56] [59] [63] [64] [66] [20] [22] [24] [26] [32] [36] [40] [42] [27] [28] [29] [30] [33] [34] [37] [38] [43] [44] [45] [49] [53] [55] [57] [58] [60] [61] [62] [65] [67] [68] [4] [69] [70] [71] Multiple Attribute Decision Making (MADM); Quality Function Deployment (QFD); Technique for Order Performance by Similarity to Ideal Solution (TOPSIS); Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE); Multi-criteria Optimization and Compromise Solution (VIKOR); Simple Multiple Attribute Rating Technique (SMART); Strengths-Weaknesses-Opportunities-Threats Analysis (SWOT); Multi-Objective Programming (MOP); Multi-Objective Model (MOM), Cluster Analysis (CA); Genetic Algorithm (GA); Inference System (IS) It must be stressed that, out of 54 selected papers, only deal with Individual fuzzy approach while 48 papers, that is 88% of all papers, deal with Integrated fuzzy approach This shows that fuzzy set theory has much greater significance when integrated with other methods and techniques from: multi criteria decision making, mathematical, statistical and artificial intelligence filed Next two sub-sections briefly present some of individual approaches and some integrated fuzzy approaches 4.2 Individual approaches Florez-Lopez (2007) [21] picked 14 most important evaluating factors out of 84 potential added-value attributes, which were based on the questionnaire response from US purchasing managers To obtain a better representation of suppliers’ ability to create value for the customers, a two-tuple fuzzy linguistic model was illustrated to combine both numerical and linguistic information Sarkar and Mohapatra (2006) [24] suggested that performance and capability were two major measures in the supplier evaluation and selection problem The authors used the fuzzy set approach to account for the imprecision involved in numerous subjective characteristics of suppliers A hypothetical case was adopted to illustrate how two best suppliers were selected with respect to four performance-based and ten capability-based factors 4.3 Integrated Fuzzy MCDM Approaches Among 54 journal articles, twenty-six papers (48.15%) formulated the supplier selection problem as various types of fuzzy multi-criteria decision making models Based on the principle behind these MCDM techniques, they can be classified them into four categories: (1) multi-attribute utility methods such as AHP and ANP; (2) outranking and ranking methods such as PROMETHEE; (3) compromise methods such as TOPSIS and VIKOR; (4) other MCDM techniques such as SMART [72] • Integrated fuzzy and multi-attribute utility methods Kahraman et al [25] applied a fuzzy AHP to select the best supplier in a Turkish white goods manufacturing company Decision makers could specify preferences about the importance of each evaluating criterion using linguistic variable Chan and Kumar [26] also used a fuzzy AHP for supplier selection as the similar case as previous mentioned paper Triangular fuzzy numbers and fuzzy synthetic extent analysis method were used to represent decision makers’ comparison judgment and decide on the final priority of different criteria • Integrated fuzzy and compromise MCDM methods Chen et al [43] presented a hierarchy model based on fuzzy sets theory to deal with the supplier selection problem The linguistic values were used to assess the ratings and weights for the supplier evaluating factors These linguistic ratings could be ex- pressed in trapezoidal or triangular fuzzy numbers The proposed model was capable of dealing with both quantitative and qualitative criteria • Integrated fuzzy and other MCDM techniques Kwong et al [48] integrated fuzzy set theory into SMART to assess the performance of suppliers The supplier assessment forms were first used to determine the scores of individual assessment items, and then the scores were input to a fuzzy expert system for the determination of supplier recommendation index Chou and Chang [49] applied a fuzzy SMART approach to evaluate the alternative suppliers in an IT hardware manufacturing company A sensitivity analysis was carried out to assess the impact of changes in the risk coefficients in terms of supplier ranking order • Integrated fuzzy and quality function deployment Bevilacqua et al [39] applied QFD approach for supplier selection A house of quality was constructed to identify the features that the purchased product should have in order to satisfy the customers’ requirements, and then to identify the relevant supplier assessment criteria The importance of product features and the relationship weightings between product features and assessment criteria were assigned in terms of fuzzy variables Finally, the potential suppliers were evaluated against these criteria 4.4 Integrated Fuzzy and Mathematical Programming Approaches Thirteen journal articles (24.07%) among 54 collected papers, formulated the supplier selection problem as various types of mathematical programming models • Integrated fuzzy and linear programming Guneri aimed to present an integrated fuzzy and linear programming approach to the problem First, linguistic values expressed in trapezoidal fuzzy numbers are applied to assess weights and ratings of supplier selection criteria Then a hierarchy multiple model based on fuzzy set theory is expressed and fuzzy positive and negative ideal solutions are used to find each supplier’s closeness coefficient And finally, a linear programming model based on the coefficients of suppliers, buyer’s budgeting, suppliers’ quality and capacity constraints is developed and order quantities are assigned to each supplier according to the linear programming model [52] Lin [31] tackles the multiple criteria and the inherent uncertainty in supplier selection This study proposes to adopt the fuzzy analytic network process (FANP) approach first to identify top suppliers by considering the effects of interdependence among selection criteria and to handle consistent and uncertain judgments FANP is then integrated with fuzzy multi-objective linear programming (FMOLP) in selecting the best suppliers for achieving optimal order allocation under fuzzy conditions • Integrated fuzzy and multi-objective programming Three very similar articles by Amid: (1) constructed the fuzzy multi-objective linear programming decision model [59]; (2) fuzzy multi-objective mixed integer linear programming model [60]; (3) weighted max–min fuzzy model [61]; on supplier selection The presented models could handle the vagueness and imprecision of input data, and help the decision makers to find out the optimal order quantity from each supplier Three objective functions with different weights were included in the model 4.5 Integrated Fuzzy and Statistical Approaches Statistical studies incorporate uncertainty and there are not many articles in the literature that utilize fuzzy set theory and statistics approaches in the supplier selection process • Integrated fuzzy, AHP, and cluster analysis Bottani and Rizzi [64] developed an integrated approach for supplier selection The approach integrated cluster analysis and fuzzy AHP to group and rank alternatives, and to progressively reduce the amount of alternatives and select the most suitable cluster Fuzzy logic was also brought in to cope with the intrinsic qualitative nature of the selection process 4.6 Integrated Fuzzy and Artificial Intelligence Approaches Artificial Intelligence based models are based on computer aided systems that in one way or another can be trained by a purchasing expert or historic data, however, the complexity of the system is not suitable for enterprises to solve the issue efficiently without high capability in advanced computer programs Although only few examples of AI methods applied to the supplier evaluation problem can be found in the literature to date it is important to investigate these methods for their potentialities One of the strengths of methods such as artificial neural networks (ANN) is that they not require formalization of the decision-making process In that respect, ANN can cope better with complexity and uncertainty than traditional methods”, because AI-based approach are designed to resemble human judgment functioning • Integrated fuzzy and GA Jain et al [67] suggested a fuzzy based approach for supplier selection The authors stated that it might be difficult for an expert to define a complete rule set for evaluating the supplier performance GA was therefore integrated to generate a number of rules inside the rule set according to the nature and type of the priorities associated with the products and their supplier’s attributes • Integrated fuzzy and Artificial Neural Networks Kuo et al [71], present the study intended to develop an intelligent supplier decision support system which is able to consider both the quantitative and qualitative factors It is composed of: (1) the collection of quantitative data such as profit and productivity; (2) a particle swarm optimization based fuzzy neural network to derive the rules for qualitative data; (3) a decision integration model for integrating both the quantitative data and fuzzy knowledge decision to achieve the optimal decision 4.7 Advantages, Disadvantages and Limitations of Approaches The last objective of this paper is to critically analyze the approaches, and try to find out some drawbacks As mentioned before, DEA technique is the most popular individually used technique with 30% in MCDM On the other hand, there are various integrated approaches for supplier selection and it was noticed that the integrated AHP approaches are more prevalent, as shown on Table All of the most popular approaches, including integrated FST approaches have their advantages, disadvantages and limitations • Advantages and disadvantages in DEA approaches DEA has attracted more attention mainly because of its robustness which is its greatest advantage when compared with other approaches But, there are two limitations of DEA approach and they can be reflected as disadvantage in real-world applications First, the practitioners may be confused with input and output criteria For example, some authors considered price/cost as an output criterion, whereas the others used it as an input criterion The second problem is due to the subjective assignment of ratings to qualitative criteria In generally, DEA is used to measure the relative efficiencies based on numerical data only Considering that the supplier selection problem involves both qualitative and quantitative criteria, DEA has been modified and extended by soft computing techniques to handle qualitative data [51] In addition, it can now be used to consider stochastic performance measures and handle imprecise data On the other hand, some authors deployed five-point and seven-point scales to rank the priorities of qualitative criteria, respectively, and some inconsistencies may occur because of the subjective judgments • Advantages and disadvantages in AHP approaches There are various integrated AHP-based approaches for supplier selection, and they are more prevalent The integrated AHP-based approaches extended classical AHP process AHP has been integrated with other soft computing techniques, including: fuzzy set theory, goal programming (GP), DEA, ANN, and multi-objective programming (MOP) Comparatively, the integrated AHP–GP approach is the most popular The major reason is that the individual techniques possess unique advantages The consistency verification operation of AHP contributes greatly to prevent inconsistency because it acts as a feedback mechanism for the decision makers to review and revise their judgments Consequently, the judgments made are guaranteed to be consistent, which is the basic ingredient for making good decisions Nevertheless, the output of AHP is merely the relative importance weightings of criteria and sub-factors In supplier selection problem, besides the weightings of alternative suppliers, the decision makers also need to consider the resource limitations For this reason, the GP can compensate for AHP It can definitely provide more useful information for the deci- sion makers Based on the analysis above, it is believed that it must be beneficial to the decision making process if AHP and GP are integrated together • Advantages and disadvantages in Fuzzy approaches There is a small number of research papers which deal with Individual fuzzy approach in supplier assessment and selection processes, but this is due to the nature of FST There are two significant limitations of FST approach and they can be reflected as disadvantages in real-world applications First, the rules of combining membership functions are known as the min-max rule for conjunctive and disjunctive reasoning These rules have a major drawback, and they are not robust at all Many researchers have proposed different rules of combining conjunctive or disjunctive clauses: instead of taking the minimum or the maximum of the membership functions, they take the arithmetic or the geometric mean It is possible, if there are enough training data, conditions and class assignments by the experts, to train the system so that it chooses the best rule that fits the way of reasoning of the expert that did the classification Another disadvantage of the rules is that they give the same importance to all factors that are to be combined To improve previously mentioned disadvantage, integrated FST approaches are implemented The first of them are the integrated FST approaches extended with classical AHP such as: Fuzzy extended AHP process (FEAHP) FEAHP is an efficient tool to handle the fuzziness of the data involved in deciding the preferences of different decision variables The linguistic level of comparisons produced by the customers and experts for each comparison are tapped in the form of triangular or trapezoidal fuzzy numbers to construct fuzzy pair-wise comparison matrices The next prosperity integrated fuzzy approach is combining FST and QFD, and should be developed to select suppliers strategically The most important information that the QFD provides is the importance weightings of evaluating criterion, which are derived by the expected ratings of requirements, together with the relationship weightings between different requirements and evaluating criterion Generally, both importance ratings of requirements and relationship weightings are determined by the decision makers arbitrarily This may result in a certain degree of inconsistency, and therefore degrade the quality of the decisions made To overcome this drawback, QFD is used to evaluate them consistently Conclusion and Future Work This paper is dedicated to 50th anniversary of Fuzzy Set Theory established by Lotfi A Zadeh in 1965, and since then fuzzy set theory has presented an inexhaustible research subject for numerous researchers in the world This paper is furthermore dedicated to Gary W Dickson who first established the list of 23 most important criteria for supplier selection, which also happened 50 years ago, in 1966 Supplier assessment and selection is one of the most important components of logistics chain, which influences the long term commitments and performance of the company Good suppliers allow enterprises to achieve good manufacturing performance and make maximum benefits for practitioners This paper presents how fuzzy set theory, fuzzy decision-making and hybrid solutions based on fuzzy can be used in the various models for supplier assessment and selection in a 50 year period For this review, the authors selected only 54 papers published in respectable journals, but only of them deal with Individual fuzzy approaches while 88% of all the selected papers deal with Integrated fuzzy approach This shows that fuzzy set theory has much greater significance when integrated with other methods and techniques from mathematical, statistical and artificial intelligence filed This paper also suggests the novel model for – Methods for supplier assessment and selection – which is to replace the Existing analytical methods for supplier assessment and selection from 2011 This research has shown that fuzzy hybrid approaches can be used to solve very complex real-world decision-making problems such as supplier assessment, ranking and supplier selection As already mentioned, DEA is most often used technique with 30% in MCDM The future work could focus on additional research on hybrid DEA supplier assessment and selection systems which integrate mathematics, statistics, and some softcomputing techniques such as evolutionary algorithms and neural networks Acknowledgments The authors acknowledge the support for research project TR 36030, funded by the Ministry of Science and Technological Development of Serbia References Tepiü, J., Tanackov, I., Stojiü, G.: Ancient logistics - Historical timeline and etymology Tehnicki Vjesnik - Technical Gazette, vol 18 (3), pp 379-384 (2011) Zadeh, L A.: Fuzzy sets, Information and Control, vol 8, pp 338-353 (1965) Simiỹ, D., Svirỵeviỹ, V., Simiỹ, S.: An approach of genetic algorithm to model supplier assessment in inbound logistics Advances in Intelligent Systems 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34(12), pp 3976-3990 (2010) 72 Chai, J., Liu, J N K., Ngai, E W T.: Application of decision-making techniques in supplier selection: A systematic review of literature Expert Systems with Applications, vol 40(10), pp 3872-3885 (2013) .. .50 Years of Fuzzy Set Theory and Models for Supplier Assessment and Selection: a Literature Review Dragan Simiỹ1*, Ilija Kovaỵeviỹ1, Vasa Svirỵeviỹ2 and Svetlana Simiỹ3 1University of Novi Sad,... including integrated FST approaches have their advantages, disadvantages and limitations • Advantages and disadvantages in DEA approaches DEA has attracted more attention mainly because of its robustness... selection and performance evaluation are of strategic importance to companies Analytical Methods for Supplier Assessment and Selection Supplier assessment and selection decisions are complicated by