Hybrid Optimization for Decision Making in an uncertain Environment

Pandian Vasant

ISBN 10: 3844303383 / ISBN 13: 9783844303384

Edité par LAP LAMBERT Academic Publishing Feb 2011, 2011

A propos de cet article

Neuware -In this book, the main significant contributions are formulation of a new non-linear membership function using fuzzy approach to capture and describe vagueness in the technological coefficients of constraints and intelligent solution methodology using hybrid meta-heuristics optimization techniques in an industrial production planning problems. The novel and innovative hybrid intelliegent such has genetic algorithms, simulated annealing, pattern search and fuzzy logic have been extensively used in this research work. Finally, it is concluded that hybrid optimization techniques are robust, less time-consuming, dependable, high quality solutions and an efficient productive tool for solving the non-linear real world problem in an uncertain industrial engineering environment. The hybrid line search with genetic algorithms and hybrid line search with simulated annealing techniques developed in this study are user friendly, easy-to-use and can serve as a teaching and research tool, besides being useful for practicing scientist, decision makers, managers and researchers in the area of industrial engineering, finance, economics and management science.Books on Demand GmbH, Überseering 33, 22297 Hamburg 244 pp. Englisch.

N° de réf. du vendeur 9783844303384

Détails bibliographiques

• Titre: Hybrid Optimization for Decision Making in an uncertain Environment
• Éditeur: LAP LAMBERT Academic Publishing Feb 2011
• Date de parution: 2011
• Langue: anglais
• ISBN à 10 chiffres: 3844303383
• ISBN à 13 chiffres: 9783844303384
• Reliure: Taschenbuch
• État de l'article: Neu

A propos de ce titre

Synopsis

In this book, the main significant contributions are formulation of a new non-linear membership function using fuzzy approach to capture and describe vagueness in the technological coefficients of constraints and intelligent solution methodology using hybrid meta-heuristics optimization techniques in an industrial production planning problems. The novel and innovative hybrid intelliegent such has genetic algorithms, simulated annealing, pattern search and fuzzy logic have been extensively used in this research work. Finally, it is concluded that hybrid optimization techniques are robust, less time-consuming, dependable, high quality solutions and an efficient productive tool for solving the non-linear real world problem in an uncertain industrial engineering environment. The hybrid line search with genetic algorithms and hybrid line search with simulated annealing techniques developed in this study are user friendly, easy-to-use and can serve as a teaching and research tool, besides being useful for practicing scientist, decision makers, managers and researchers in the area of industrial engineering, finance, economics and management science.