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Showing 6 results for Multiple Criteria Decision Making

A. Amid, S.h. Ghodsypour,
Volume 19, Issue 4 (12-2008)

  Supplier selection is one of the most important activities of purchasing departments. This importance is increased even more by new strategies in a supply chain, because of the key role suppliers perform in terms of quality, costs and services which affect the outcome in the buyer’s company. Supplier selection is a multiple criteria decision making problem in which the objectives are not equally important. In practice, vagueness and imprecision of the goals, constraints and parameters in this problem make the decision making complicated. Simultaneously, in this model, vagueness of input data and varying importance of criteria are considered. In real cases, where Decision- Makers (DMs) face up to uncertain data and situations, the proposed model can help DMs to find out the appropriate ordering from each supplier, and allows purchasing manager(s) to manage supply chain performance on cost, quality, on time delivery, etc. An additive weighted model is presented for fuzzy multi objective supplier selection problem with fuzzy weights. The model is explained by an illustrative example.

Mir. B. Aryanezhad, M.j. Tarokh, M.n. Mokhtarian, F. Zaheri,
Volume 22, Issue 1 (3-2011)

  Multiple criteria decision making (MCDM) problem is one of the famous different kinds of decision making problems. In more cases in real situations, determining the exact values for MCDM problems is difficult or impossible. So, the values of alternatives with respect to the criteria or / and the values of criteria weights, are considered as fuzzy values (fuzzy numbers). In such conditions, the conventional crisp approaches for solving MCDM problems tend to be less effective for dealing with the imprecise or vagueness nature of the linguistic assessments. In this situation, the fuzzy MCDM methods are applied for solving MCDM problems. In this paper, we propose a fuzzy TOPSIS (for Order Preference by Similarity to Ideal Solution) method based on left and right scores for fuzzy MCDM problems. To show the applicability of the proposed method, two numerical examples are presented. As a result, our proposed method is precise, easy use and practical for solving MCDM problem with fuzzy data. Moreover, the proposed method considers the decision makers (DMs) preference in the decision making process. It seems that the proposed fuzzy TOPSIS method is flexible and easy to use and has a low computational volume .

Mahdi Yousefi Nejad Attari, Mohhamad Reza Bageri, Ensiyeh Neishabouri,
Volume 23, Issue 3 (9-2012)

Decision making about outsourcing or insourcing of manufacturing activities is a type of multiple criteria decision making (MCDM) problem, which requires considering quantitative and qualitative factors as evaluation criteria simultaneously. Therefore, a suitable MCDM method can be useful in this area as it can consider the interactions among quantitative and qualitative criteria. The analytic network process (ANP) is a relatively new MCDM method which can deal with different kinds of interactions systematically. Moreover, the Decision Making Trial and Evaluation Laboratory (DEMATEL) method is able to convert the relations between cause and effect of criteria into a visual structural model as well as handling the inner dependences within a set of criteria. However both ANP method and DEMATEL techniques in their original forms are incapable of capturing the uncertainty during value judgment elicitation. To overcome this problem, here, a new and effective model is proposed based on combining fuzzy ANP and fuzzy DEMATEL for decision making about outsourcing or insourcing of manufacturing activities in uncertain conditions. Data from a case study is used to illustrate the usefulness and applicably of the proposed method.
Ali Mohaghar, Mojtaba Kashef, Ehsan Kashef Khanmohammadi,
Volume 25, Issue 2 (5-2014)

Considering the major change occurred in business cells from plant to “chain” and the critical need to choose the best partners to form the supply chain for competing in today’s business setting, one of the vital decisions made at the early steps of constructing a business is supplier selection. Given the fact that the early decisions are inherently strategic and therefore hard and costly to change, it’s been a point of consideration for industries to select the right supplier. It’s clear that different criteria must be investigated and interfered in deciding on the best partner(s) among the alternatives. Thereupon the problem might be regarded as a multiple criteria decision making (MCDM) problem. There are a variety of techniques to solve a MCDM problem. In this paper we propose a novel technique by combination of decision making trial and evaluation laboratory and graph theory and matrix approach techniques. Eventually, the results are compared to SAW technique and discussed to come to a conclusion.
Dr Akbar Esfahanipour, Mr Hamed Davari Ardakani,
Volume 26, Issue 4 (11-2015)

In today’s competitive business situation, performance evaluation of firms is an extremely important concern of all the people who are typically stakeholders of the business game. In case of holding companies, this is a more important issue since the parent firm must permanently control the situation of its subsidiaries in their sectors to make appropriate investment decisions. This paper develops a multicriteria decision making (MCDM) approach for evaluating performance of firms considering financial and productivity criteria.. We adopt Fuzzy Analytic Hierarchy Process (FAHP) method to determine the relative importance of evaluation criteria, taking the vagueness and imprecision of human judgments into consideration. Then, we employ the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) for ranking of firms. Afterward, this paper enjoys benefit of using Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) to assess the validity of the obtained ranking results. Our approach was applied to a holding company listed in Tehran Stock Exchange (TSE) as a real case. The analysis of ranking results revealed advantages of combining these MCDM methods.


Roghaye Hemmatjou, Nasim Nahavandi, Behzad Moshiri,
Volume 27, Issue 3 (9-2016)

In most of the multi–criteria decision–analysis (MCDA) problems in which the Choquet integral is used as aggregation function, the coefficients of Choquet integral (capacity) are not known in advance. Actually, they could be calculated by capacity definition methods. In these methods, the preference information of decision maker (DM) is used to constitute a possible solution space. The methods which are based on optimizing an objective function most often suffer from three drawbacks. Firstly, the selection of the ultimate solution from solution set is arbitrarily done. Secondly, the solution may provide more information than whatever proposed by DM. Thirdly, DM may not fully interpret the results. Robust capacity definition methods are proposed to overcome these kinds of drawbacks, on the other hand these methods do not consider evenness (uniformity) which is a major property of capacity. Since in capacity definition methods, the preference information on only a subset of alternatives called reference alternatives, is used, defining the capacity as uniform as possible could improve its capability in evaluating non–reference alternatives. This paper proposes an algorithm to define a capacity that is based only on the preference information of DM and consequently is representative. Furthermore, it improves evenness of capacity and consequently its reliability in evaluating non–reference alternatives. The algorithm is used to evaluate power plant projects. Power plant projects are of the most important national projects in Iran and a major portion of national capital is invested on them, so these projects should be scientifically evaluated in order to figure out their performance. Case–specific criteria are considered in addition to general criteria used in project performance evaluation. The evaluation results obtained from proposed algorithm are compared with those of the most representative utility function method.

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