Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Combining belief functions when evidence conflicts
Decision Support Systems
Applications of Belief Functions in Business Decisions: A Review
Information Systems Frontiers
Combining belief functions based on distance of evidence
Decision Support Systems
Computers and Operations Research
A knowledge-based approach to adversarial decision making: Research Articles
International Journal of Intelligent Systems
Information Sciences: an International Journal
Robotics and Computer-Integrated Manufacturing
Computers & Mathematics with Applications
Expert Systems with Applications: An International Journal
Extension of fuzzy TOPSIS method based on interval-valued fuzzy sets
Applied Soft Computing
Expert Systems with Applications: An International Journal
Evaluating Sensor Reliability in Classification Problems Based on Evidence Theory
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A hybrid MCDM model for strategic vendor selection
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Assessment of E-Commerce security using AHP and evidential reasoning
Expert Systems with Applications: An International Journal
Review: A state-of the-art survey of TOPSIS applications
Expert Systems with Applications: An International Journal
Knowledge-Based Systems
A Model for Decision Making with Missing, Imprecise, and Uncertain Evaluations of Multiple Criteria
International Journal of Intelligent Systems
Application of decision-making techniques in supplier selection: A systematic review of literature
Expert Systems with Applications: An International Journal
A biologically inspired solution for fuzzy shortest path problems
Applied Soft Computing
A new method to determine basic probability assignment from training data
Knowledge-Based Systems
Supplier selection using AHP methodology extended by D numbers
Expert Systems with Applications: An International Journal
Environmental impact assessment based on D numbers
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A new decision-making method by incomplete preferences based on evidence distance
Knowledge-Based Systems
Hi-index | 12.06 |
Supplier selection is a multi-criterion decision making problem under uncertain environments. Hence, it is reasonable to hand the problem in fuzzy sets theory (FST) and Dempster Shafer theory of evidence (DST). In this paper, a new MCDM methodology, using FST and DST, based on the main idea of the technique for order preference by similarity to an ideal solution (TOPSIS), is developed to deal with supplier selection problem. The basic probability assignments (BPA) can be determined by the distance to the ideal solution and the distance to the negative ideal solution. Dempster combination rule is used to combine all the criterion data to get the final scores of the alternatives in the systems. The final decision results can be drawn through the pignistic probability transformation. In traditional fuzzy TOPSIS method, the quantitative performance of criterion, such as crisp numbers, should be transformed into fuzzy numbers. The proposed method is more flexible due to the reason that the BPA can be determined without the transformation step in traditional fuzzy TOPSIS method. The performance of criterion can be represented as crisp number or fuzzy number according to the real situation in our proposed method. The numerical example about supplier selection is used to illustrate the efficiency of the proposed method.