Design a new mixed expert decision aiding system using fuzzy ELECTRE III method for vendor selection
ABSTRACT Any decision process deals with two different concerns as its cornerstones, evaluating the alternatives and ranking them based on their performances. In any decision process, the former phase is usually the premise of the latter one. Alternatives’ evaluation is the concept that largely depends on the experts and their expertise, which increase uncertainty in the decision-making process. In addition to all proposed methods for having the experts’ knowledge as evaluations of the alternatives, utilizing expert decision support systems (EDSS) can be a sensible response to such a need. Having evaluated the alternatives in the first phase of a decision-making process, the second phase of the process deals with the ranking the alternatives based on their performances obtained from the first phase. In this paper, we discuss the architecture of a fuzzy system including both modules, utilizing fuzzy concept for dealing with the uncertainty of the problem. Concerning the problem we had been dealt with, our system comprises a fuzzy evaluation module, which is a fuzzy expert system and an appropriate tool for evaluating the existing alternatives promptly and smoothly, without the imposed time delays by the experts to propose their comments and the uncertainty of such expertise-based comments, and a fuzzy ranking module, which is a fuzzy version of ELECTRE III method ranking the alternatives based on their outranking relations and by considering the existing uncertainty in their performances. This way the final ranking is resulted from an independent fuzzy system, which has considered the existing uncertainty in the evaluations not once but twice. Our proposed system has been applied to a real case of vendor selection process in one of the greatest and the most famous companies in the Iranian oil industry, OIEC, and the results are discussed.
- SourceAvailable from: Wojciech Sałabun
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- "The following examples illustrate the following: the SAW (Simple Additive Weighing)    , the very wellknown and widely used AHP (Analytic Hierarchy Process) [12, 17–25], and ANP (Analytic Network Process)        . Other well-known methods, such as TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)       , ELECTRE (ELimination and Choice Expressing REality)     , and PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations)      are not strictly linear; however, they assume and use global "
ABSTRACT: This paper presents a new, nonlinear, multicriteria, decision-making method: the characteristic objects (COMET). This approach, which can be characterized as a fuzzy reference model, determines a measurement standard for decision-making problems. This model is distinguished by a constant set of specially chosen characteristic objects that are independent of the alternatives. After identifying a multicriteria model, this method can be used to compare any number of decisional objects (alternatives) and select the best one. In the COMET, in contrast to other methods, the rank-reversal phenomenon is not observed. Rank-reversal is a paradoxical feature in the decision-making methods, which is caused by determining the absolute evaluations of considered alternatives on the basis of the alternatives themselves. In the Analytic Hierarchy Process (AHP) method and similar methods, when a new alternative is added to the original alternative set, the evaluation base and the resulting evaluations of all objects change. A great advantage of the COMET is its ability to identify not only linear but also nonlinear multicriteria models of decision makers. This identification is based not on a ranking of component criteria of the multicriterion but on a ranking of a larger set of characteristic objects (characteristic alternatives) that are independent of the small set of alternatives analyzed in a given problem. As a result, the COMET is free of the faults of other methods.Applied Computational Intelligence and Soft Computing 11/2014; DOI:10.1155/2014/536492
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- "Fuzzy theory has been used extensively by the researchers in combination with other methods such as the AHP, ANP, TOPSIS ELECTRE, PROMETHEE, DEA, linear programming, goal programming, multiple objective programming etc. for supplier selection (e.g. Montazer et al. 2009; Wang et al. 2009; Azadeh and Alem 2010; Amid et al. 2011; Chen et al. 2011a; Vinodh et al. 2011; Bhattacharya et al. 2014; Jadidi et al. 2014). "
ABSTRACT: The main aim of this research is to demonstrate strategic supplier performance evaluation of a UK-based manufacturing organisation using an integrated analytical framework. Developing long term relationship with strategic suppliers is common in today's industry. However, monitoring suppliers’ performance all through the contractual period is important in order to ensure overall supply chain performance. Therefore, client organisations need to measure suppliers’ performance dynamically and inform them on improvement measures. Although there are many studies introducing innovative supplier performance evaluation frameworks and empirical researches on identifying criteria for supplier evaluation, little has been reported on detailed application of strategic supplier performance evaluation and its implication on overall performance of organisation. Additionally, majority of the prior studies emphasise on lagging factors (quality, delivery schedule and value / cost) for supplier selection and evaluation. This research proposes both leading (organisational practices, risk management, environmental and social practices) and lagging factors for supplier evaluation and demonstrates a systematic method for identifying those factors with the involvement of relevant stakeholders and process mapping. The contribution of this article is a real-life case-based action research utilizing an integrated analytical model that combines Quality Function Deployment and the Analytic Hierarchy Process method for suppliers’ performance evaluation. The effectiveness of the method has been demonstrated through number of validations (e.g. focus group, business results, and statistical analysis). Additionally, the study reveals that enhanced supplier performance results positive impact on operational and business performance of client organisation.International Journal of Production Economics 09/2014; DOI:10.1016/j.ijpe.2014.09.021 · 2.75 Impact Factor
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- "Kumar and Rajender Singh (2008) developed a rulebased expert system for selection of piloting to assist die designers and process planners working in stamping industries. Montazer et al. (2009) developed an expert decision support system (EDSS) using a fuzzy version of ELECTRE III method for ranking the alternatives based on the experts' knowledge. This EDSS was applied to a vendor selection process in an Iranian oil industry. "
ABSTRACT: This paper presents the development of a model based decision support system with a case study on solving the supplier selection problem in a chemical processing industry. For the evaluation and selection of supplier, the analytical hierarchy process (AHP) and grey relational analysis (GRA) were used. The intention of the study is to propose an appropriate platform for process industries in selecting suppliers, which was tested with an electroplating industry during the course of development. The sensitivity analysis was performed in order to improve the robustness of the results with regard to the relative importance of the evaluation criteria and the parameters of the evaluation process. Finally, a practical implementation study was carried out to reveal the procedure of the proposed system and identify the suitable supplier with detailed discussions about the benefits and limitations..