scholarly journals International market selection: a MABA based EDAS analysis framework

2021 ◽  
Vol 12 (1) ◽  
pp. 99-124
Author(s):  
Sarfaraz Hashemkhani Zolfani ◽  
Ali Ebadi Torkayesh ◽  
Fatih Ecer ◽  
Zenonas Turskis ◽  
Jonas Šaparauskas
Keyword(s):  
Decision Making ◽  
Food Supply ◽  
International Market ◽  
International Markets ◽  
Market Analysis ◽  
Decision Makers ◽  
Market Selection ◽  
Decision Factors ◽  
Multiple Attribute ◽  
Decision Making Tool

Research background: International market selection is an essential issue for big companies that supply food products. Different types of decision factors and different characteristics of different international markets have brought up a complicated decision-making problem for food supply companies. In order to select the most suitable and profitable market, food supply companies have to consider several qualitative and quantitative factors, including social, political, economic, and ecological aspects. Purpose of the article: In order to overcome international market selection issues, the current study develops a novel integrated decision-making tool. Methods: A novel decision-making model of market analysis is developed as an extended model of Market Attractiveness and Business Attractiveness (MABA) analysis based on the Multiple Attribute Decision Making (MADM). To improve the MABA analysis model, we combine the EDAS method with MABA analysis to empower decision-makers in food supply companies to evaluate several international markets and select the most profitable market for their products. Findings & value added: In this study, we first identified the most important and frequently used decision factors for market analysis problems within MABA analysis under two categories: market attractiveness and business attractiveness. To show the proposed methodology's applicability and feasibility, we perform a case study for a food supply company in Iran that supplies products to Middle East and Asian countries. In order to investigate the reliability of the obtained results, we perform a sensitivity analysis concerning the importance of involved decision factors. The proposed decision-making tool results suggest that the model can be used as a reliable tool for market analysis problems. To sum up the long-term value of the study, we have developed a novel decision-making tool using MABA analysis and the EDAS method. No study integrates any MCDM methods with MABA analysis to the best of our knowledge. Integration of EDAS method with MABA analysis empowers decision-makers in market selection division to use more systematic methods for evaluating several markets.

CPT-MABAC method for spherical fuzzy multiple attribute group decision making and its application to green supplier selection

2021 ◽  
pp. 1-11
Author(s):  
Huiyuan Zhang ◽  
Guiwu Wei ◽  
Xudong Chen
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Supplier Selection ◽  
Group Decision ◽  
Comparison Method ◽  
Entropy Method ◽  
Decision Makers ◽  
Green Supplier Selection ◽  
Multiple Attribute ◽  
Psychological Perceptions

The green supplier selection is one of the popular multiple attribute group decision making (MAGDM) problems. The spherical fuzzy sets (SFSs) can fully express the complexity and fuzziness of evaluation information for green supplier selection. Furthermore, the classic MABAC (multi-attributive border approximation area comparison) method based on the cumulative prospect theory (CPT-MABAC) is designed, which is an optional method in reflecting the psychological perceptions of decision makers (DMs). Therefore, in this article, we propose a spherical fuzzy CPT-MABAC (SF-CPT-MABAC) method for MAGDM issues. Meanwhile, considering the different preferences of DMs to attribute sets, we obtain the objective weights of attributes through entropy method. Focusing on the current popular problems, this paper applies the proposed method for green supplier selection and proves for green supplier selection based on SF-CPT-MABAC method. Finally, by comparing existing methods, the effectiveness of the proposed method is certified.

scholarly journals Some q-Rung Dual Hesitant Fuzzy Heronian Mean Operators with Their Application to Multiple Attribute Group Decision-Making

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 472 ◽  
Author(s):  
Yuan Xu ◽  
Xiaopu Shang ◽  
Jun Wang ◽  
Wen Wu ◽  
Huiqun Huang
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Group Decision Making ◽  
Group Decision ◽  
Decision Makers ◽  
Hesitant Fuzzy Sets ◽  
Multiple Attribute ◽  
A New Technique ◽  
Supplier Selection Problem ◽  
Heronian Mean

The q-rung orthopair fuzzy sets (q-ROFSs), originated by Yager, are good tools to describe fuzziness in human cognitive processes. The basic elements of q-ROFSs are q-rung orthopair fuzzy numbers (q-ROFNs), which are constructed by membership and nonmembership degrees. As realistic decision-making is very complicated, decision makers (DMs) may be hesitant among several values when determining membership and nonmembership degrees. By incorporating dual hesitant fuzzy sets (DHFSs) into q-ROFSs, we propose a new technique to deal with uncertainty, called q-rung dual hesitant fuzzy sets (q-RDHFSs). Subsequently, we propose a family of q-rung dual hesitant fuzzy Heronian mean operators for q-RDHFSs. Further, the newly developed aggregation operators are utilized in multiple attribute group decision-making (MAGDM). We used the proposed method to solve a most suitable supplier selection problem to demonstrate its effectiveness and usefulness. The merits and advantages of the proposed method are highlighted via comparison with existing MAGDM methods. The main contribution of this paper is that a new method for MAGDM is proposed.

scholarly journals Hybrid Multiattribute Group Decision Making Based on Intuitionistic Fuzzy Information and GRA Method

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Jian Guo
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Decision Makers ◽  
Ideal Solution ◽  
Decision Matrix ◽  
Intuitionistic Fuzzy ◽  
Multiple Attribute ◽  
Intuitionistic Fuzzy Entropy ◽  
Fuzzy Group Decision Making

Hybrid multiple attribute group decision making involves ranking and selecting competing courses of action available using attributes to evaluate the alternatives. The decision makers assessment information can be expressed in the form of real number, interval-valued number, linguistic variable, and the intuitionistic fuzzy number. All these evaluation information can be transformed to the form of intuitionistic fuzzy numbers. A combined GRA with intuitionistic fuzzy group decision-making approach is proposed. Firstly, the hybrid decision matrix is standardized and then transformed into an intuitionistic fuzzy decision matrix. Then, intuitionistic fuzzy averaging operator is utilized to aggregate opinions of decision makers. Intuitionistic fuzzy entropy is utilized to obtain the entropy weights of the criteria, respectively. After intuitionistic fuzzy positive ideal solution and intuitionistic fuzzy negative ideal solution are calculated, the grey relative relational degree of alternatives is obtained and alternatives are ranked. In the end, a numerical example illustrates the validity and applicability of the proposed method.

Material Selection Using a Novel Multiple Attribute Decision Making Method

2012 ◽  
pp. 252-266
Author(s):  
R. V. Rao ◽  
B. K. Patel
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Decision Maker ◽  
Design Process ◽  
Material Selection ◽  
Multiple Attribute Decision Making ◽  
Decision Makers ◽  
Selection Problem ◽  
Multiple Attribute ◽  
Selection Of

Selection of a most appropriate material is a very important task in design process of every product. There is a need for simple, systematic, and logical methods or mathematical tools to guide decision makers in considering a number of selection attributes and their interrelations and in making right decisions. This paper proposes a novel multiple attribute decision making (MADM) method for solving the material selection problem. The method considers the objective weights of importance of the attributes as well as the subjective preferences of the decision maker to decide the integrated weights of importance of the attributes. Furthermore, the method uses fuzzy logic to convert the qualitative attributes into the quantitative attributes. Two examples are presented to illustrate the potential of the proposed method.

Reducing the Risk of Wrong Choice in Group Decision Making by Optimal Weight Allocating to Decision Makers

2018 ◽  
Vol 7 (2) ◽  
pp. 1-23 ◽  
Author(s):  
Mohammad Azadfallah
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Potential Risk ◽  
Group Decision ◽  
Decision Makers ◽  
Topsis Method ◽  
Numerical Examples ◽  
Multiple Attribute ◽  
Key Issues ◽  
The Ideal

How to determine a weight for decision makers (DMs) is one of the key issues in Multiple Attribute Group Decision Making (MAGDM). While, some experts (or DMs) clearly wiser and more powerful in such matters than others, it has often seen that experts play their roles with same weights of importance. Meanwhile, it will lead to the wrong choice (or decision risk) and loss of values. Since, in the absence of any other standards about how to reduce this potential risk for bias, in this article, based on judgment matrices and error analysis, the author presents two new algorithm taken from crisp (the correlation-based approach) and interval (the ideal-based approach) TOPSIS method, respectively. Finally, two numerical examples are given to demonstrate the feasibility of the developed method.

Some Interval-Valued Pythagorean Fuzzy Einstein Weighted Averaging Aggregation Operators and Their Application to Group Decision Making

2018 ◽  
Vol 29 (1) ◽  
pp. 393-408 ◽  
Author(s):  
Khaista Rahman ◽  
Saleem Abdullah ◽  
Muhammad Sajjad Ali Khan
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Aggregation Operator ◽  
Aggregation Operators ◽  
Decision Makers ◽  
Weighted Averaging ◽  
Fuzzy Information ◽  
Multiple Attribute ◽  
Interval Valued

Abstract In this paper, we introduce the notion of Einstein aggregation operators, such as the interval-valued Pythagorean fuzzy Einstein weighted averaging aggregation operator and the interval-valued Pythagorean fuzzy Einstein ordered weighted averaging aggregation operator. We also discuss some desirable properties, such as idempotency, boundedness, commutativity, and monotonicity. The main advantage of using the proposed operators is that these operators give a more complete view of the problem to the decision makers. These operators provide more accurate and precise results as compared the existing method. Finally, we apply these operators to deal with multiple-attribute group decision making under interval-valued Pythagorean fuzzy information. For this, we construct an algorithm for multiple-attribute group decision making. Lastly, we also construct a numerical example for multiple-attribute group decision making.

scholarly journals Big data analytics and international market selection: An exploratory study

2020 ◽  
Vol 10 (2) ◽  
Author(s):  
Jonathan Calof ◽  
Wilma Viviers
Keyword(s):  
Big Data ◽  
Decision Maker ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Organizational Factors ◽  
International Market ◽  
Decision Makers ◽  
Market Selection ◽  
Selection Decisions ◽  
The Impact

A great deal of information is available on international trade flows and potentialmarkets. Yet many exporters do not know how to identify, with adequate precision, thosemarkets that hold the greatest potential. Even if they have access to relevant information, thesheer volume of information often makes the analytical process complex, time-consuming andcostly. An additional challenge is that many exporters lack an appropriate decision-makingmethodology, which would enable them to adopt a systematic approach to choosing foreignmarkets. In this regard, big-data analytics can play a valuable role. This paper reports on thefirst two phases of a study aimed at exploring the impact of big-data analytics on internationalmarket selection decisions. The specific big-data analytics system used in the study was theTRADE-DSM (Decision Support Model) which, by screening large quantities of marketinformation obtained from a range of sources identifies optimal product‒market combinationsfor a country, industry sector or company. Interviews conducted with TRADE-DSM users aswell as decision-makers found that big-data analytics (using the TRADE-DSM model) didimpact international market-decision. A case study reported on in this paper noted thatTRADE-DSM was a very important information source used for making the company’sinternational market selection decision. Other interviewees reported that TRADE-DSMidentified countries (that were eventually selected) that the decision-makers had not previouslyconsidered. The degree of acceptance of the TRADE-DSM results appeared to be influenced byTRADE-DSM user factors (for example their relationship with the decision-maker andknowledge of the organization), decision-maker factors (for example their experience andknowledge making international market selection decisions) and organizational factors (forexample senior managements’ commitment to big data and analytics). Drawing on the insightsgained in the study, we developed a multi-phase, big-data analytics model for internationalmarket selection.

scholarly journals Selection of Industrial Robots for Automation Applications in Multiple Attribute Decision Making Environment using the Analytical Network Process

2018 ◽  
Vol 7 (3.12) ◽  
pp. 392 ◽  
Author(s):  
N Rishi Kanth ◽  
A Srinath ◽  
J Suresh Kumar
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Industrial Robots ◽  
Decision Makers ◽  
Analytical Network Process ◽  
Multiple Attribute ◽  
Network Process ◽  
Complex Relationships ◽  
Robot Selection ◽  
Selection Of

Analytical Network process (ANP), is applied here as a decision making technique for the selection of appropriate robots for industrial and automation applications. The core motivation of applying, in particular, the ANP technique is that robot selection is dependent upon a number of attributes and criteria which have strong influences/interdependencies upon each other. The ANP, as a multiple attribute decision making (MADM) technique for robot selection, captures the effects of these cross hierarchical dependencies, and appropriately maps the influences within the clusters and between the various alternatives. Simultaneously, the technique does not include the assumption of independence of higher-level elements from lower level elements and about the independence of the elements within a level. First, a set of attributes, which influence the selection of the robots, are identified. Next, using the various steps of ANP, viz., pair wise comparisons matrices and priority vectors determination and the development of the super-matrix the global weights of the attributes with respect to other attributes are determined. The final alternatives are then rated as per the graduated weights of the respective attributes. Thus, a comprehensive solution towards selection of robots enabling the decision-makers to suitably understand the complex relationships of the relevant qualitative and quantitative attributes in the decision-making is obtained. The technique is also illustrated using detailed analysis for a specific case of decision making between three robot suppliers and selection of appropriate robot from alternatives. In order to get more insight into relationships among various attributes and their effect on decision makers, the sensitivity analysis of the results with respect to determinant level attributes is carried out.   

scholarly journals Dombi Aggregation Operators of Linguistic Cubic Variables for Multiple Attribute Decision Making

Information ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 188 ◽  
Author(s):  
Xueping Lu ◽  
Jun Ye
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Optimal Choice ◽  
Decision Makers ◽  
Linguistic Variable ◽  
Operational Parameter ◽  
Arithmetic Average ◽  
Weighted Arithmetic ◽  
Multiple Attribute ◽  
Dombi Operations

A linguistic cubic variable (LCV) is comprised of interval linguistic variable and single-valued linguistic variable. An LCV contains decision-makers’ uncertain and certain linguistic judgments simultaneously. The advantage of the Dombi operators contains flexibility due to its changeable operational parameter. Although the Dombi operations have been extended to many studies to solve decision-making problems; the Dombi operations are not used for linguistic cubic variables (LCVs) so far. Hence, the Dombi operations of LCVs are firstly presented in this paper. A linguistic cubic variable Dombi weighted arithmetic average (LCVDWAA) operator and a linguistic cubic variable Dombi weighted geometric average (LCVDWGA) operator are proposed to aggregate LCVs. Then a multiple attribute decision making (MADM) method is developed in LCV setting on the basis of LCVDWAA and LCVDWGA operators. Finally, two illustrative examples about the optimal choice problems demonstrate the validity and the application of this method.

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