Theory and Practice of Uncertain Programming

Baoding Liu

388 pages, parution le 20/06/2002

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Résumé

Real-life decisions are usually made in the state of uncertainty (randomness, fuzziness, roughness, etc.). How do we model optimization problems in uncertain environments? How do we solve these models? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory. It includes numerous modeling ideas, hybrid intelligent algorithms, and various applications in transportation problem, inventory system, facility location & allocation, capital budgeting, topological optimization, vehicle routing problem, redundancy optimization, and scheduling. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.

Contents

I FUNDAMENTALS
Mathematical programming
Genetic algorithms
Neutral networks
II STOCHASTIC PROGRAMMING
Random variables
Stochastic expected value models
Stochastic
Stochastic dependant-chance programming
III FUZZY PROGRAMMING
Fuzzy variables
Fuzzy expected value models
Fuzzy chance-constrained programming
Fuzzy dependant-chance programming
Fuzzy programming with fuzzy decisions
IV ROUGH PROGRAMMING
Rough variables
Rough programming
V FUZZY RANDOM PROGRAMMING
Fuzzy random variables
Fuzzy random expected value models
Fuzzy random chance-constrained programming
Fuzzy random dependant-chance programming
VI RANDOM FUZZY PROGRAMMING
Random fuzzy variables
Random fuzzy expected value models
Random fuzzy chance-constrained programming
Random fuzzy dependant-chance programming
VII GENERAL PRINCIPLES
Multifold uncertainty
Uncertain programming

Caractéristiques techniques

	PAPIER
Éditeur(s)	Physica-Verlag
Auteur(s)	Baoding Liu
Parution	20/06/2002
Nb. de pages	388
Format	16 x 24
Couverture	Relié
Poids	729g
Intérieur	Noir et Blanc
EAN13	9783790814903