Graph-related Optimization and Decision Support Systems(English, Hardcover, Krichen Saoussen)

Graph-related Optimization and Decision Support Systems(English, Hardcover, Krichen Saoussen)

  • Krichen Saoussen
Publisher:John Wiley & SonsISBN 13: 9781848217430ISBN 10: 1848217439

Paperback & Hardcover deals ―

Amazon IndiaGOFlipkart ₹ 16564SnapdealGOSapnaOnlineGOJain Book AgencyGOBooks Wagon₹180Book ChorGOCrosswordGODC BooksGO

e-book & Audiobook deals ―

Amazon India GOGoogle Play Books GOAudible GO

* Price may vary from time to time.

* GO = We're not able to fetch the price (please check manually visiting the website).

Know about the book -

Graph-related Optimization and Decision Support Systems(English, Hardcover, Krichen Saoussen) is written by Krichen Saoussen and published by ISTE Ltd and John Wiley & Sons Inc. It's available with International Standard Book Number or ISBN identification 1848217439 (ISBN 10) and 9781848217430 (ISBN 13).

Constrained optimization is a challenging branch of operations research that aims to create a model which has a wide range of applications in the supply chain, telecommunications and medical fields. As the problem structure is split into two main components, the objective is to accomplish the feasible set framed by the system constraints. The aim of this book is expose optimization problems that can be expressed as graphs, by detailing, for each studied problem, the set of nodes and the set of edges. This graph modeling is an incentive for designing a platform that integrates all optimization components in order to output the best solution regarding the parameters' tuning. The authors propose in their analysis, for optimization problems, to provide their graphical modeling and mathematical formulation and expose some of their variants. As a solution approaches, an optimizer can be the most promising direction for limited-size instances. For large problem instances, approximate algorithms are the most appropriate way for generating high quality solutions. The authors thus propose, for each studied problem, a greedy algorithm as a problem-specific heuristic and a genetic algorithm as a metaheuristic.