Cooperative and noncooperative multi-level programming
In: Operations research, computer science interfaces series 48
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In: Operations research, computer science interfaces series 48
In: Studies in Fuzziness and Soft Computing 64
This book presents some of the most important concepts in noncooperative games and cooperative games and introduces as well the latest advances in the new field of game theory in fuzzy and multiobjective environments. Several game representations and solution concepts are introduced which take the amgiguity and the multiplicity of objectives into consideration. The book also demonstrates the computational methods applied for obtaining the solutions. The reader can learn much about fuzzy and multiobjective games for conflict resolution in most realistic settings without prior mathematical sophistication
In: Cooperative and Noncooperative Multi-Level Programming; Operations Research/Computer Science Interfaces Series, S. 11-24
In: Cooperative and Noncooperative Multi-Level Programming; Operations Research/Computer Science Interfaces Series, S. 83-179
In: Cooperative and Noncooperative Multi-Level Programming; Operations Research/Computer Science Interfaces Series, S. 181-237
In: Cooperative and Noncooperative Multi-Level Programming; Operations Research/Computer Science Interfaces Series, S. 25-82
In: Cooperative and Noncooperative Multi-Level Programming; Operations Research/Computer Science Interfaces Series, S. 1-9
In: Journal of multi-criteria decision analysis, Band 11, Heft 3, S. 125-137
ISSN: 1099-1360
AbstractTwo major approaches to deal with randomness or ambiguity involved in mathematical programming problems have been developed. They are stochastic programming approaches and fuzzy programming approaches. In this paper, we focus on multiobjective linear programming problems with random variable coefficients in objective functions and/or constraints. Using chance constrained programming techniques, the stochastic programming problems are transformed into deterministic ones. As a fusion of stochastic approaches and fuzzy ones, after determining the fuzzy goals of the decision maker, interactive fuzzy satisficing methods to derive a satisficing solution for the decision maker by updating the reference membership levels is presented. Copyright © 2003 John Wiley & Sons, Ltd.