Heuristics for ranking players in a round robin tournament
Computers and Operations Research
A data envelopment model for aggregating preference rankings
Management Science
Comparison among three analytical methods for knowledge communities group-decision analysis
Expert Systems with Applications: An International Journal
Compromise ratio method for fuzzy multi-attribute group decision making
Applied Soft Computing
A fuzzy clustering methodology for linguistic opinions in group decision making
Applied Soft Computing
A fuzzy-based military officer performance appraisal system
Applied Soft Computing
A causal analytical method for group decision-making under fuzzy environment
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
MP-OWA: The most preferred OWA operator
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Aggregating preference ranking with fuzzy Data Envelopment Analysis
Knowledge-Based Systems
A DEA-inspired procedure for the aggregation of preferences
Expert Systems with Applications: An International Journal
Ranking efficient decision-making units in data envelopment analysis using fuzzy concept
Computers and Industrial Engineering
Computers and Industrial Engineering
A systematic decision-making approach for the optimal product-service system planning
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fuzzy data envelopment analysis: A discrete approach
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Hi-index | 12.06 |
Group decision making is the study of identifying and selecting alternatives based on the values and preferences of the decision maker. Making a decision implies that there are several alternative choices to be considered. This paper uses the concept of Data Envelopment Analysis to introduce a new mathematical method for selecting the best alternative in a group decision making environment. The introduced model is a multi-objective function which is converted into a multi-objective linear programming model from which the optimal solution is obtained. A numerical example shows how the new model can be applied to rank the alternatives or to choose a subset of the most promising alternatives.