An extension of fuzzy TOPSIS for a group decision making with an application to tehran stock exchange

In financial markets, investors attempt to maximize their profits within a constructed portfolio with the aim of optimizing the tradeoffs between risk and return across the many stocks. This requires proper handling of conflicting factors, which can benefit from the domain of multiple criteria decision making (MCDM). However, the indexes and factors representing the stock performance are often imprecise or vague and this should be represented by linguistic terms characterized by fuzzy numbers. The aim of this research is to first develop three group MCDM methods, then use them for selecting undervalued stocks by dint of financial ratios and subjective judgments of experts. This study proposes three versions of fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution): conventional TOPSIS (C-TOPSIS), adjusted TOPSIS (A-TOPSIS) and modified TOPSIS (M-TOPSIS) where a new fuzzy distance measure, derived from the confidence level of the experts and fuzzy performance ratings have been included in the proposed methods. The practical aspects of the proposed methods are demonstrated through a case study in the Tehran stock exchange (TSE), which is timely given the need for investors to select undervalued stocks in untapped markets in the anticipation of easing economic sanctions from a change in recent government leadership.     

A hybrid fuzzy MCDM approach to machine tool selection

The selection of the appropriate machine tools for a manufacturing company is one of the important points to achieving high competitiveness in the market. Besides, an appropriate choice of machine tools is very important as it helps to realize full production quickly. Today’s market offers many more choices for machine tool alternatives. There are also many factors one should consider as part of the appropriate machine tool selection process, including productivity, flexibility, compatibility, safety, cost, etc. Consequently evaluation procedures involve several objectives and it is often necessary to compromise among possibly conflicting tangible and intangible factors. For these reasons, multiple criteria decision making (MCDM) has been found to be a useful approach to solve this kind of problem. Most of the MCDM models are basically mathematical and ignore qualitative and often subjective considerations. The use of fuzzy set theory allows incorporating qualitative and partially known information into the decision model. This paper describes a fuzzy technique for order preference by similarity to ideal solution (TOPSIS) based methodology for evaluation and selection of vertical CNC machining centers for a manufacturing company in Istanbul, Turkey. The criteria weights are calculated by using the fuzzy AHP (analytical hierarchy process).     

Group decision making process for supplier selection with VIKOR under fuzzy environment

During recent years, how to determine suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of supplier selection is a complex multi-criteria problem including both quantitative and qualitative factors which may be in conflict and may also be uncertain. The VIKOR method was developed to solve multiple criteria decision making (MCDM) problems with conflicting and non-commensurable (different units) criteria, assuming that compromising is acceptable for conflict resolution, the decision maker wants a solution that is the closest to the ideal, and the alternatives are evaluated according to all established criteria. In this paper, linguistic values are used to assess the ratings and weights for these factors. These linguistic ratings can be expressed in trapezoidal or triangular fuzzy numbers. Then, a hierarchy MCDM model based on fuzzy sets theory and VIKOR method is proposed to deal with the supplier selection problems in the supply chain system. A numerical example is proposed to illustrate an application of the proposed model.     

Logistics tool selection with two-phase fuzzy multi criteria decision making: A case study for personal digital assistant selection

Efficient logistics and supply chain management are enabled through the use of efficient information technologies (IT). The mobile logistics tools represent the IT interface in the supply chain. This paper aims to aid decision makers to identify the most appropriate mobile logistics tools and to achieve this aim, several evaluation criteria are identified to evaluate logistics tools, and a fuzzy axiomatic design (FAD) based group decision-making method is adopted to perform the evaluation in two phases. In the first phase of pre-assessment, alternatives that cannot meet basic requirements and the defined threshold are eliminated. In the second phase of selection, the remaining alternatives are more meticulously evaluated. Criteria weights are determined using fuzzy analytic hierarchy process (AHP) and another fuzzy multi-criteria decision-making (MCDM) technique, namely fuzzy technique for order preference by similarity to ideal solution (TOPSIS), is applied in the second phase to compare the outcome of FAD. A case study is provided in order to demonstrate the potential of the proposed methodology. Personal digital assistants (PDAs) with integrated barcode scanner that are available in the Turkish market are evaluated.     

A combined fuzzy AHP and fuzzy TOPSIS based strategic analysis of electronic service quality in healthcare industry

Service sector is under pressure to deliver continuing performance and quality improvement while being customer-focused. In recent terms, there exists web based or electronic service quality (e-sq) concept. With the birth of electronic commerce, it has become important to be able to monitor and enhance e-sq. Therefore, this study will examine the e-sq concept and determine the key components of e-sq. The e-sq framework is employed by the aid of service quality (SERVQUAL) methodology as the theoretical instrument. Finally, proposed e-sq framework is illustrated with a web service performance example of healthcare sector in Turkey by using a combined multiple criteria decision making (MCDM) methodology containing fuzzy analytic hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS). The work presented in this paper shows the applicability of the e-sq framework in explaining the complexity of aspects observed in the implementation of healthcare services via internet.     

Weapon selection using the AHP and TOPSIS methods under fuzzy environment

The weapon selection problem is a strategic issue and has a significant impact on the efficiency of defense systems. On the other hand, selecting the optimal weapon among many alternatives is a multi-criteria decision-making (MCDM) problem. This paper develops an evaluation model based on the analytic hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS), to help the actors in defence industries for the selection of optimal weapon in a fuzzy environment where the vagueness and subjectivity are handled with linguistic values parameterized by triangular fuzzy numbers. The AHP is used to analyze the structure of the weapon selection problem and to determine weights of the criteria, and fuzzy TOPSIS method is used to obtain final ranking. A real world application is conducted to illustrate the utilization of the model for the weapon selection problem. The application could be interpreted as demonstrating the effectiveness and feasibility of the proposed model.     

A new method selection approach for fuzzy group multicriteria decision making

Fuzzy multicriteria decision making (MCDM) has been widely used in ranking a finite number of decision alternatives characterized by fuzzy assessments with respect to multiple criteria. In group decision settings, different fuzzy group MCDM methods often produce inconsistent ranking outcomes for the same problem. To address the ranking inconsistency problem in fuzzy group MCDM, this paper develops a new method selection approach for selecting a fuzzy group MCDM method that produces the most preferred group ranking outcome for a given problem. Based on two group averaging methods, three aggregation procedures and three defuzzification methods, 18 fuzzy group MCDM methods are developed as an illustration to solve the general fuzzy MCDM problem that requires cardinal ranking of the decision alternatives. The approach selects the group ranking outcome of a fuzzy MCDM method which has the highest consistency degree with its corresponding ranking outcomes of individual decision makers. An empirical study on the green bus fuel technology selection problem is used to illustrate how the approach works. The approach is applicable to large-scale group multicriteria decision problems where inconsistent ranking outcomes often exist between different fuzzy MCDM methods.     

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.     

An extended GRA method for MCDM with interval-valued triangular fuzzy assessments and unknown weights

Multiple criteria decision making (MCDM) is the process of ranking the feasible alternatives and selecting the best one by considering multiple criteria. Owing to the complexity, fuzziness and uncertainties of the objective things, the criterion values often take the form of linguistic variables, which can be expressed in interval-valued triangular fuzzy numbers. The purpose of this paper is to develop an extended grey relational analysis (GRA) method for solving MCDM problems with interval-valued triangular fuzzy numbers and unknown information on criterion weights. In order to determine the criterion weights, some optimization models based on the basic idea of traditional GRA method are established. Then, calculation steps of extended GRA method for MCDM are given. Finally, a numerical example is shown to verify the developed method and to demonstrate its practicality and feasibility.     

Trapezoidal fuzzy multi-number and its application to multi-criteria decision-making problems

In this paper, the concept of trapezoidal fuzzy multi-number (TFM-number) is proposed and some desired operational laws with properties are introduced. In the TFM-number, the occurrences are more than one with the possibility of the same or the different membership functions and the TFM-number is an extension of both fuzzy number set and fuzzy set, allowing the repeated occurrences of any element. Also, aim of this paper is to investigate a multiple criteria decision-making (MCDM) method under TFM-number environment. To construct this method, we first introduce some operational laws on TFM-number based on t-norm and s-norm. Then, TFM-number arithmetic and geometric operators are proposed. Finally, we develop an MCDM method and apply to an MCDM problem to verify the introduced decision-making methods.

     

A state of the art literature review of VIKOR and its fuzzy extensions on applications

Multi criteria decision making (MCDM) is one of the research areas of operations research and management science which has widely studied by researchers and practitioners. It finds a compromise solution for evaluating and ranking alternatives from the best to the worst under conflicting criteria with respect to decision maker(s) preferences. In a compromise approach, the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR; that means multi-criteria optimization and compromise solution) continues to be applied satisfactorily across different application areas. This paper conducts a state-of-the-art literature review to categorize, analyze and interpret the current research on VIKOR applications. It also discusses the extensions of VIKOR applied in fuzzy environments. A total of 343 papers are classified into 13 different application areas and a number of sub-application areas. Furthermore, all papers are also categorized with respect to publication year, published journal, country of origin, application type (real case study vs empirical study), and version of fuzzy sets used. This comprehensive literature review provides an insight for researchers and practitioners on VIKOR applications in terms of showing current state and potential areas for future attempts to be focused in the future.     

Asset portfolio optimization using fuzzy mathematical programming

By morphing mean-variance optimization (MVO) portfolio model into semi-absolute deviation (SAD) model, we apply multi criteria decision making (MCDM) via fuzzy mathematical programming to develop comprehensive models of asset portfolio optimization (APO) for the investors’ pursuing either of the aggressive or conservative strategies.     

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.     

Best value concessionaire selection through a fuzzy logic system

Best value is the ultimate goal in the development of infrastructure projects through public private partnerships. This necessitates a best value approach in selecting the right private partner (i.e., the concessionaire) that involves evaluating alternative concessionaires against multiple conflicting criteria of varying degrees of importance and uncertainty. Correspondingly, this paper has developed a quantitative best value concessionaire selection system. The essence of this system lies in (1) the establishment of four-package evaluation criteria that can effectively measure the concessionaire’s capability and “predict” its future performance toward achieving the government’s best value objectives and (2) the development of a fuzzy-composite scoring and ranking method that ensures the right “tradeoff” between these criteria by relatively weighting fuzzy scores and logically aggregating evaluation results. This system would not only provide the government integrated assessments of alternative concessionaires so as to award the contract to the right concessionaire whose proposal is perceived to be able to maximize the outcome of the project, but also satisfy the requirements of the legal decision in order to withstand legal challenges concerning the public contract awarded in a best value approach. A hypothetical case study is provided to demonstrate the application of this best value concessionaire selection system.     

Extension of fuzzy TOPSIS method based on interval-valued fuzzy sets

Decision making is one of the most complex administrative processes in management. In circumstances where the members of the decision making team are uncertain in determining and defining the decision making criteria, fuzzy theory provides a proper tool to encounter with such uncertainties. However, if decision makers cannot reach an agreement on the method of defining linguistic variables based on the fuzzy sets, the interval-valued fuzzy set theory can provide a more accurate modeling. In this paper the interval-valued fuzzy TOPSIS method is presented aiming at solving MCDM problems in which the weights of criteria are unequal, using interval-valued fuzzy sets concepts.     

An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information

In engineering design, selecting the most suitable material for a particular product is a typical multiple criteria decision making (MCDM) problem, which generally involves several feasible alternatives and conflicting criteria. In this paper, we aim to propose a novel approach based on interval-valued intuitionistic fuzzy sets (IVIFSs) and multi-attributive border approximation area comparison (MABAC) for handling material selection problems with incomplete weight information. First, individual evaluations of experts concerning each alternative are aggregated to construct the group interval-valued intuitionistic fuzzy (IVIF) decision matrix. Consider the situation where the criteria weight information is partially known, a linear programming model is established for determining the criteria weights. Then, an extended MABAC method within the IVIF environment is developed to rank and select the best material. Finally, two application examples are provided to demonstrate the applicability and effectiveness of the proposed IVIF-MABAC approach. The results suggest that for the automotive instrument panel, polypropylene is the best, for the hip prosthesis, Co–Cr alloys-wrought alloy is the optimal option. Finally, based on the results, comparisons between the IVIF-MABAC and other relevant representative methods are presented. It is observed that the obtained rankings of the alternative materials are good agreement with those derived by the past researchers.     

A hybrid fuzzy group decision support framework for advanced-technology prioritization at NASA

The prioritization of advanced-technology projects at the National Aeronautic and Space Administration (NASA) is a difficult task. This difficulty is due to the multiple and often conflicting objectives in addition to the inherent technical complexities and valuation uncertainties involved in the assessment process. As such, a systematic and transparent decision support framework is needed to guide the assessment process, shape the decision outcomes and enable confident choices to be made. Methods for solving Multi-Criteria Decision Making (MCDM) problems have been widely used to select a finite number of alternatives generally characterized by multiple conflicting criteria. However, applying these methods is becoming increasingly difficult for technology assessment in the space industry because there are many emerging risks for which information is not available and decisions are made under significant uncertainty. In this paper, we propose a hybrid fuzzy group decision support framework for technology assessment at NASA. The proposed objective framework is comprised of two modules. In the first module, the complicated structure of the assessment criteria and alternatives are represented and evaluated with the Analytic Network Process (ANP). In the second module, the alternative advanced-technology projects are ranked using a customized fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We demonstrate the applicability of the proposed framework through a case study at the Kennedy Space Center.     

A soft computing method of performance evaluation with MCDM based on interval-valued fuzzy numbers

This study presented a new performance evaluation method for tackling fuzzy multicriteria decision-making (MCDM) problems based on combining VIKOR and interval-valued fuzzy sets. The performance evaluation problem often exists in complex administrative processes in which multiple evaluation criteria, subjective/objective assessments and fuzzy conditions have to be taken into consideration simultaneously in management. Here, the subjective, imprecise, inexact and uncertain evaluation processes are modeled as fuzzy numbers by means of linguistic terms, as fuzzy theory can provide an appropriate tool to deal with such uncertainties. However, the presentation of linguistic expressions in the form of ordinary fuzzy sets is not clear enough [15] and [21]. Interval-valued fuzzy sets can provide more flexibility [4] and [14] to represent the imprecise/vague information that results, and it can also provide a more accurate modeling. This paper presents the interval-valued fuzzy VIKOR, which aims to solve MCDM problems in which the weights and performances of criteria are unequal by using the concepts of interval-valued fuzzy sets. A case study for evaluating the performances of three major intercity bus companies from an intercity public transport system is conducted to illustrate the effectiveness of the method.     

A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers

It is well known that “green” principles and strategies have become vital for companies as the public awareness increased against their environmental impacts. A company’s environmental performance is not only related to the company’s inner environmental efforts, but also it is affected by the suppliers’ environmental performance and image. For industries, environmentally responsible manufacturing, return flows, and related processes require green supply chain (GSC) and accompanying suppliers with environmental/green competencies. During recent years, how to determine suitable and green suppliers in the supply chain has become a key strategic consideration. Therefore this paper examines GSC management (GSCM) and GSCM capability dimensions to propose an evaluation framework for green suppliers. However, the nature of supplier selection is a complex multi-criteria problem including both quantitative and qualitative factors which may be in conflict and may also be uncertain. The identified components are integrated into a novel hybrid fuzzy multiple criteria decision making (MCDM) model combines the fuzzy Decision Making Trial and Evaluation Laboratory Model (DEMATEL), the Analytical Network Process (ANP), and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) in a fuzzy context. A case study is proposed for green supplier evaluation in a specific company, namely Ford Otosan.     

Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology

Energy planning is a complex issue which takes technical, economic, environmental and social attributes into account. Selection of the best energy technology requires the consideration of conflicting quantitative and qualitative evaluation criteria. When decision-makers’ judgments are under uncertainty, it is relatively difficult for them to provide exact numerical values. The fuzzy set theory is a strong tool which can deal with the uncertainty in case of subjective, incomplete, and vague information. It is easier for an energy planning expert to make an evaluation by using linguistic terms. In this paper, a modified fuzzy TOPSIS methodology is proposed for the selection of the best energy technology alternative. TOPSIS is a multicriteria decision making (MCDM) technique which determines the best alternative by calculating the distances from the positive and negative ideal solutions according to the evaluation scores of the experts. In the proposed methodology, the weights of the selection criteria are determined by fuzzy pairwise comparison matrices. The methodology is applied to an energy planning decision-making problem.