A Problem-based Selection of Multi-attribute Decision-making Methods

Different multi-attribute decision-making (MADM) methods often produce different outcomes for selecting or ranking a set of decision alternatives involving multiple attributes. This paper presents a new approach to the selection of compensatory MADM methods for a specific cardinal ranking problem via sensitivity analysis of attribute weights. In line with the context-dependent concept of informational importance, the approach examines the consistency degree between the relative degree of sensitivity of individual attributes using an MADM method and the relative degree of influence of the corresponding attributes indicated by Shannon's entropy concept. The approach favors the method that has the highest consistency degree as it best reflects the decision information embedded in the problem data set. An empirical study of a scholarship student selection problem is used to illustrate how the approach can validate the ranking outcome produced by different MADM methods. The empirical study shows that different problem data sets may result in a different method being selected. This approach is particularly applicable to large-scale cardinal ranking problems where the ranking outcome of different methods differs significantly.     

A De Novo multi-approaches multi-criteria decision making technique with an application in performance evaluation of material handling device

Application of multiple conventional approaches to a particular multi-criteria decision making (MCDM) problem often suffers rank reversal giving rise to confusion and ambiguity in appropriate decision making. To eradicate the confusion, this paper proposes a De Novo multi-approaches multi-criteria decision making method namely Technique of Precise Order Preference (TPOP). The TPOP first examines the inconsistency in the ranking order of the alternatives of a MCDM problem by using multiple conventional approaches. If inconsistency/rank reversal in ranking order of the alternatives exists then TPOP, using advanced version of entropy weighting method introduced in this research work, measures weights of the final selection values of conventional approaches. Subsequently, TPOP based on these weights and final selection values computes precise selection indices (PSI) that determines accurate ranking order for the alternatives. The proposed technique is illustrated by two real life examples on material handling device (MHD) ranking and selection problems. The first example is initially solved using five conventional integrated fuzzy multi-criteria decision making techniques (FMCDMs) whereas the second example is taken from previous researchers’ works. The results obtained using TPOP justify the validity, applicability and requirements of the proposed technique. The study shows that the proposed multi-approaches, multi-criteria decision making technique can be a useful and effective model in MCDM.     

Application of a new combined intuitionistic fuzzy MCDM approach based on axiomatic design methodology for the supplier selection problem

Evaluating and selecting a suitable supplier is a complex problem which involves a number of different criteria. In literature, there are various multi-criteria decision making (MCDM) methods available with their own characteristic features. The focus of this study is intuitionistic fuzzy (IF) MCDM methods which have attracted much attention from academics and practitioners in recent years. IF sets are widely used to tackle imprecise and uncertain decision information in decision making due to their capability of accommodating the hesitation in human decision processes. This study proposes a new integrated methodology that is used for the first time in the literature. This approach consists of intuitionistic fuzzy analytic hierarchy process (IFAHP), an MCDM technique, for determining the weights of supplier evaluation criteria, and the concept of intuitionistic fuzzy axiomatic design (IFAD) principles for ranking competing supplier alternatives with respect to their overall performance. Decision makers’ assessments and opinions are extended to the IF environment in this approach and furthermore, the group decision making (GDM) approach is utilized in order to overcome uncertainties and vagueness, minimize the partiality of decision process and to avoid bias. This study contributes to supplier selection and IF sets literature by providing a combined framework based on IFAHP and IFAD methodology for the first time. To assess the validity of the proposed integrated IF MCDM approach, a case study from Turkey is provided. This study can be useful to researchers in better understanding the supplier selection problem theoretically, as well as to organizations in designing better satisfying supplier evaluation systems.     

Developing a fuzzy TOPSIS approach based on subjective weights and objective weights

Multiple criteria decision making (MCDM) is widely used in ranking one or more alternatives from a set of available alternatives with respect to multiple criteria. Inspired by MCDM to systematically evaluate alternatives under various criteria, we propose a new fuzzy TOPSIS for evaluating alternatives by integrating using subjective and objective weights. Most MCDM approaches consider only decision maker’s subjective weights. However, the end-user attitude can be a key factor. We propose a novel approach that involves end-user into the whole decision making process. In this proposed approach, the subjective weights assigned by decision makers (DM) are normalized into a comparable scale. In addition, we also adopt end-user ratings as an objective weight based on Shannon’s entropy theory. A closeness coefficient is defined to determine the ranking order of alternatives by calculating the distances to both ideal and negative-ideal solutions. A case study is performed showing how the propose method can be used for a software outsourcing problem. With our method, we provide decision makers more information to make more subtle decisions.     

Data transformation in the evidential reasoning-based decision making process

This paper describes the application of an evidential reasoning (ER)‐based decision making process to multiple‐criteria decision making (MCDM) problems having both quantitative and qualitative criteria. The ER approach is based on the decision theory and the theory of evidence and it uses the concept of ‘degree of belief’ to assess decision alternatives on each attribute. When faced with MCDM problems, evaluation and selection or ranking of alternatives appear to be both challenging and vital to arrive at a rational and robust decision. In the presence of both qualitative and quantitative evaluations in an MCDM problem, it is necessary, when using the ER‐based decision making process, to transform or convert quantitative data into a belief structure using a number of grades so that the converted belief structure and the original quantitative data are equivalent in values or utilities. This paper suggests three scenarios for data transformation and examines how the ranking of decision alternatives is changed when different scenarios of data transformation are used. Ranking of UK universities using the ER approach is illustrated as an example.     

Multicriteria fuzzy decision making based on interval-valued intuitionistic fuzzy sets

In this paper, we present a new method for multicriteria fuzzy decision making based on interval-valued intuitionistic fuzzy sets, where interval-valued intuitionistic fuzzy values are used to represent evaluating values of the decision-maker with respect to alternatives. First, we propose a new method for ranking interval-valued intuitionistic fuzzy values. Based on the proposed fuzzy ranking method of interval-valued intuitionistic fuzzy values, we propose a new method for multicriteria fuzzy decision making. The proposed multicriteria fuzzy decision making method outperforms Ye’s method (2009) due to the fact that the proposed method can overcome the drawback of Ye’s method (2009), where the drawback of Ye’s method is that it can not distinguish the ranking order between alternatives in some situations. The proposed method provides us with a useful way for dealing with multicriteria fuzzy decision making problems based on interval-valued intuitionistic fuzzy sets.     

Parting curve selection and evaluation using an extension of fuzzy MCDM approach

Parting curve selection and evaluation plays an important role in mold design. Multiple criteria decision-making (MCDM) is an effective tool for evaluating and ranking problems involving multiple criteria. In order to select suitable parting curve, several criteria need to be taken into account. Therefore, this paper proposes an extension of fuzzy MCDM approach to solve parting curve selection problem. In the proposed model, the ratings of alternatives and importance weights of criteria for parting curve selection are expressed in linguistic terms. The membership functions of the final fuzzy evaluation value in the proposed model are developed based on the linguistic expressions. To make the procedure easier and more practical, the normalized weighted ratings are defuzzified into crisp values by using a new maximizing set and minimizing set ranking approach to determine the ranking order of alternatives. An example of parting curve evaluation and selection is given. The results show that the proposed approach is very effective in selecting the optimal parting curve for the molded part. Finally, this paper compares the proposed approach with another fuzzy MCDM approach to demonstrate its advantages and applicability.     

A goal programming approach to group decision-making with three formats of incomplete preference relations

This paper proposes a goal programming approach to solve the group decision-making problem where the preference information about alternatives provided by decision makers can be represented in three formats, i.e., incomplete multiplicative preference relations, incomplete fuzzy preference relations and incomplete linguistic preference relations. In the approach, a transformation function is introduced to transform the incomplete linguistic preference relation into an incomplete fuzzy preference relation. To narrow the gap between the collective opinion and each decision maker’s opinion, a liner goal programming model is constructed to integrate the three different formats of incomplete preference relations and to compute the collective ranking values of the alternatives. Thus, the ranking order of alternatives or selection of the most desirable alternative(s) is obtained directly according to the computed collective ranking values. A numerical example is also used to illustrate the feasibility and the applicability of the proposed approach.     

A new fuzzy weighted average (FWA) method based on left and right scores: An application for determining a suitable location for a gas oil station

The selection of a facility location from alternative locations is a multiple criteria decision making (MCDM) problem including both quantitative and qualitative criteria. In many real-life cases, determining the exact values for MCDM problems, and especially for facility location selection 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) such that the conventional crisp approaches for solving facility location selection problems and other MCDM problems tend to be less effective for dealing with the imprecise or vagueness nature of the linguistic assessments. In such conditions, fuzzy MCDM methods are applied for facility location selection problem and other fuzzy MCDM problems. In this paper, we propose a new fuzzy weighted average (FWA) method based on left and right scores for fuzzy MCDM problems. Moreover, we apply the proposed method to a real application. As a result, we found that the proposed method is practical for facility location selection problems. Besides, it seems that the proposed FWA method is very accurate, flexible, simple, and easy to use when compared to other versions of the FWA method.     

A new flexible and reliable interval valued fuzzy VIKOR method based on uncertainty risk reduction in decision making process: An application for determining a suitable location for digging some pits for municipal wet waste landfill

Facility location selection problem is one of the challenging and famous kinds of MCDM problems including both quantitative and qualitative criteria. For each Multiple Criteria Decision Making (MCDM) problem, when the ratings of alternatives with respect to the criteria and/or the values of criteria’s weights are presented by Interval Valued Fuzzy Numbers (IVFNs), the conventional fuzzy MCDM methods (Type-1 fuzzy MCDM methods) tend to be less effective. Therefore, the IVF-MCDM (Interval Valued Fuzzy MCDM) methods should be applied for solving such fuzzy MCDM problems. In this paper, we propose an IVF-VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method based on uncertainty risk reduction in decision making process. By using such method, the reliability of the captured decisions in an IVF decision making problem is significantly increased. The proposed method is applied for solving two numerical examples that the former of which is a real application problem related to selecting a suitable location for digging some pits for municipal wet waste landfill in one of the largest cities in Iran. The second numerical example is presented with an aim of comparing our method with the two other IVF-MCDM methods. As a result, we found out the proposed method is reliable and practical for the facility location selection problems and other MCDM problems. Moreover, the proposed method has a considerable accuracy and is flexible and easy to use.     

A Model for Decision Making with Missing,Imprecise, and Uncertain Evaluations of Multiple Criteria

In real‐life multicriteria decision making (MCDM) problems, the evaluations against some criteria are often missing, inaccurate, and even uncertain, but the existing theories and models cannot handle such evaluations well. To address the issue, this paper extends the Dempster–Shafer (DS)/analytic hierarchy process (DS/AHP) approach of MCDM to handle three types of ambiguous evaluations: missing, interval‐valued, and ambiguous lottery evaluations. In our extension, the aggregation of criteria's evaluation takes the following six steps: (i) calculate the expected evaluation interval and the ambiguity degree of each group of decision alternatives regarding each criterion, (ii) from them to obtain the preference degree of each group of decision alternatives, (iii) apply the DS/AHP method to obtain the mass function distribution of each group of decision alternatives, (iv) use the Dempster's rule of combination to get the overall mass function of each group of decision alternatives with respect to all criteria, (v) according to the overall mass function to count the belief function and the plausibility function of each decision alternative, and (vi) set the overall preference ordering of decision alternatives by our regret‐avoid ambiguous principle and then find the optimal solution. Finally, we give an example of real estate investment to illustrate how our approach is employed to deal with real‐life MCDM problems.     

A new fuzzy MCDM approach based on centroid of fuzzy numbers

This paper proposes a new fuzzy MCDM (FMCDM) approach based on centroid of fuzzy numbers for ranking of alternatives. The FMCDM approach allows decision makers (DMs) to evaluate alternatives using linguistic terms such as very high, high, slightly high, medium, slightly low, low, very low or none rather than precise numerical values, allows them to express their opinions independently, and also provides an algorithm to aggregate the assessments of alternatives. Three numerical examples are investigated using the FMCDM approach to illustrate its applications. It is shown that the FMCDM approach offers a flexible, practical and effective way of group decision making.     

A better score function for multiple criteria decision making in fuzzy environment with criteria choice under risk

The aim of this paper is to develop a new method for solving multiple criteria decision making (MCDM) problems in fuzzy environment to overcome all the deficiencies observed in the existing methods. For this purpose a weighted geometric aggregation operator (WGAO) and a new score function based on interval valued intuitionistic fuzzy soft set of root type (IVIFSSRT) are defined and some interesting theoretical properties of these tools are established. It is shown that interval valued intuitionistic fuzzy set of root type is a generalization of interval valued intuitionistic fuzzy set. A new method for ranking the alternatives of the MCDM problem based on WGAO and the new score function is presented and an algorithm is developed for this purpose. The working of the algorithm is explained with an example and the efficiency and superiority of the tools and new method are established with the help of a critical comparison study. It is shown that the proposed method works efficiently in solving the MCDM problem in fuzzy environment.     

Multicriteria Decision Making with Cognitive Limitations: A DS/AHP‐Based Approach

In real life, sometimes multicriteria decision making (MCDM) problems are dealt with inevitably under cognitive limitations of human's minds. However, few existing models can directly solve MCDM problems of this kind. Thus, to address the issue, this paper proposes a novel approach, which can: (i) handle the cognitive limitations in MCDM problems by distinguishing the case of complete criteria (i.e., there are no hidden cognitive factors that can deviate rational decisions) from the case of incomplete criteria (i.e., there are some hidden cognitive factors that can deviate rational decisions); (ii) differentiate incomplete and complete relative ranking of the groups of decision alternatives (DAs) over a criterion; and (iii) solve the imprecise and uncertain evaluation of criterion weight as well as the ambiguous evaluations of the groups of DAs regarding a given criterion. Hence, we give a measure to consider the influence of cognitive limitations and give two methods to reduce the influence of cognitive limitations when a decision making needs more rational. Moreover, we illustrate our approach by solving a real‐life problem of estate investment. Finally, we give some experimental results about the reduction of the required number of knowledge judgments in our method compared with the previous methods.     

Fuzzy Preference Domains and Their Use for Selecting an Electronic Flight Bag for Flight Crews

A method for the multicriteria evaluation of alternatives based on preferences specified in fuzzy domains is proposed. The concept of fuzzy scales of the criteria and preference domains is formalized. An algorithm for the fuzzy ranking of alternatives is proposed. A free computer implementation of the fuzzy ranking algorithm is available on the portal ws-dss.com of web services for decision support systems. The capabilities of the ranking procedure based on the preferences specified in fuzzy domains are demonstrated using the important problem of selecting an electronic flight bag (EFB) for flight crews as an example.     

An integrated fuzzy multi-criteria approach for the performance evaluation of multiple manufacturing plants

The performance evaluation is regarded as a multiple criteria decision making (MCDM) problem and has a significant impact on the operations of the enterprise. This paper develops an integrated MCDM approach that combines the voting method and the fuzzy TOPSIS (technique for order preference by similarity to ideal solution) method to evaluate the performance of multiple manufacturing plants in a fuzzy environment. Fuzzy TOPSIS helps decision-makers carry out analysis and comparisons in ranking their preference of the alternatives with vague or imprecise data. Since the evaluation result is often greatly affected by the weights used in the evaluation process, the voting method is used in this study to determine the appropriate criteria weights. A case study demonstrating the applicability of the proposed model is presented. The case company is the world’s largest manufacturer of power supplies. It has three primary manufacturing bases located in Wujiang, Dongguan, and Tianjin, China. The proposed approach is used to evaluate the performance of the company’s five manufacturing plants in Wujiang, which produce switch power, telecom power, DC/DC converters, uninterruptible power systems (UPS) and AC/DC adapters.     

Combining VIKOR with GRA techniques to evaluate service quality of airports under fuzzy environment

The aim of this paper was to present an effective approach for evaluating service quality of Northeast-Asian international airports by conducting customer surveys. In general, evaluation of service quality is a complex multicriteria decision-making (MCDM) problem; therefore, a complex decision process is often involved in which multiple requirements and fuzzy conditions have to be taken into consideration simultaneously. By combining concepts of VIKOR and grey relational analysis (GRA), a new fuzzy MCDM method was proposed to deal with the evaluation of service quality problems in the international airports. This model was solved by an effective algorithm, which incorporated the decision-maker’s attitude and/or preference for customers’ assessments on weights and performance ratings of each criterion. An empirical study for evaluating service quality of seven major Northeast-Asian international airports was put forth to illustrate an application of the proposed model. The study results showed that this approach is an effective means for tackling MCDM problems involving subjective assessments of qualitative attributes in a fuzzy environment.     

A new linguistic MCDM method based on multiple-criterion data fusion

Multiple-criteria decision-making (MCDM) is concerned with the ranking of decision alternatives based on preference judgements made on decision alternatives over a number of criteria. First, taking advantage of data fusion technology to comprehensively consider each criterion data is a reasonable idea to solve the MCDM problem. Second, in order to efficiently handle uncertain information in the process of decision making, some well developed mathematical tools, such as fuzzy sets theory and Dempster Shafer theory of evidence, are used to deal with MCDM. Based on the two main reasons above, a new fuzzy evidential MCDM method under uncertain environments is proposed. The rating of the criteria and the importance weight of the criteria are given by experts’ judgments, represented by triangular fuzzy numbers. Then, the weights are transformed into discounting coefficients and the ratings are transformed into basic probability assignments. The final results can be obtained through the Dempster rule of combination in a simple and straight way. A numerical example to select plant location is used to illustrate the efficiency of the proposed method.     

A new flexible and reliable IVF-TOPSIS method based on uncertainty risk reduction in decision making process

One of the challenging and famous types of MCDM (Multiple Criteria Decision Making) problems that includes both quantitative and qualitative criteria is Facility location selection problem. For the common fuzzy MCDM problems (Type-1 fuzzy MCDM problems), the ratings of alternatives with respect to the criteria or/and the values of criteria weights, are expressed by the common fuzzy numbers. However, in the majority of cases, determining the exact membership degree for each element of the fuzzy sets which are considered for the ratings of alternatives with respect to the criteria or/and the values of criteria weights as a number in interval [0,1], is difficult. In this situation, the ratings of alternatives with respect to the criteria or/and the values of criteria weights, are expressed by the IVFNs (Interval Valued Fuzzy Numbers) and thereby the IVF-MCDM (Interval Valued Fuzzy MCDM) methods should be used. In this paper, the authors propose an IVF-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method based on uncertainty risk reduction in decision making process. By using this method, the reliability of the captured decisions in an IVF decision making problem is significantly increased. The proposed method is applied for solving a real application problem related to selecting a suitable location for digging some pits for municipal wet waste landfill in one of the largest cities in Iran. The proposed method is also compared with another IVF-TOPSIS method. As a result, the authors concluded that in addition to benefits such as simplicity and ease of use that exist in the previous IVF-TOPSIS methods, the proposed method has a significant reliability and flexibility and is practical for facility location selection problems and other IVF-MCDM problems.     

A novel group decision making method with intuitionistic fuzzy preference relations for RFID technology selection

A more scientific decision making process for radio frequency identification (RFID) technology selection is important to increase success rate of RFID technology application. RFID technology selection can be formulated as a kind of group decision making (GDM) problem with intuitionistic fuzzy preference relations (IFPRs). This paper develops a novel method for solving such problems. First, A technique for order preference by similarity to ideal solution (TOPSIS) based method is presented to rank intuitionistic fuzzy values (IFVs). To achieve higher group consensus as well as possible, we construct an intuitionistic fuzzy linear programming model to derive experts’ weights. Depending on the construction of membership and non-membership functions, the constructed intuitionistic fuzzy linear programming model is solved by three kinds of approaches: optimistic approach, pessimistic approach and mixed approach. Then to derive the ranking order of alternatives from the collective IFPR, we extend quantifier guided non-dominance degree (QGNDD) and quantifier guided dominance degree (QGDD) to intuitionistic fuzzy environment. A new two-phase ranking approach is designed to generate the ordering of alternatives based on QGNDD and QGDD. Thereby, the corresponding method is proposed for the GDM problems with IFPRs. Some generalizations on the constructed intuitionistic fuzzy linear programming model are further discussed. At length, the validity of the proposed method is illustrated with a real-world RFID technology selection example.