Operations Research
A new approach for ranking fuzzy numbers by distance method
Fuzzy Sets and Systems
Extensions of the TOPSIS for group decision-making under fuzzy environment
Fuzzy Sets and Systems
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Agent learning in supplier selection models
Decision Support Systems - Special issue: Decision theory and game theory in agent design
Supplier selection and order lot sizing modeling: A review
Computers and Operations Research
A hybrid approach to supplier selection for the maintenance of a competitive supply chain
Expert Systems with Applications: An International Journal
Using the analytic hierarchy process to rank foreign suppliers based on supply risks
Computers and Industrial Engineering
A fuzzy supplier selection model with the consideration of benefits, opportunities, costs and risks
Expert Systems with Applications: An International Journal
An integrated method for finding key suppliers in SCM
Expert Systems with Applications: An International Journal
A fuzzy model for supplier selection in quantity discount environments
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Supplier selection based on a neural network model using genetic algorithm
IEEE Transactions on Neural Networks
An effective supplier selection method for constructing a competitive supply-relationship
Expert Systems with Applications: An International Journal
An intelligent supplier evaluation, selection and development system
Applied Soft Computing
Application of decision-making techniques in supplier selection: A systematic review of literature
Expert Systems with Applications: An International Journal
Supplier selection using AHP methodology extended by D numbers
Expert Systems with Applications: An International Journal
Integration of semi-fuzzy SVDD and CC-Rule method for supplier selection
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
The selection supplier problem has received a lot of attention from academics in recent years. Several models were developed in the literature, combining consolidated operations research and artificial intelligence methods and techniques. However, the tools presented in the literature neglected learning and adaptation, since this decision making process is approached as a static one rather than a highly dynamic process. Delays, lack of capacity, quality related issues are common examples of dynamic aspects that have a direct impact on long-term relationships with suppliers. This paper presents a novel method based on the integration of influence diagram and fuzzy logic to rank and evaluate suppliers. The model was developed to support managers in exploring the strengths and weaknesses of each alternative, to assist the setting of priorities between conflicting criteria, to study the sensitivity of the behavior of alternatives to changes in underlying decision situations, and finally to identify a preferred course of action. To be effective, the computational implementation of the method was embedded into an information system that includes several functionalities such as supply chain simulation and supplier's databases. A case study in the biodiesel supply chain illustrates the effectiveness of the developed method.