logo

EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link:  https://ebookbell.com/faq 


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Graphrelated Optimization And Decision Theory 1st Edition Saoussen Krichen

  • SKU: BELL-4765628
Graphrelated Optimization And Decision Theory 1st Edition Saoussen Krichen
$ 31.00 $ 45.00 (-31%)

4.1

20 reviews

Graphrelated Optimization And Decision Theory 1st Edition Saoussen Krichen instant download after payment.

Publisher: Wiley-ISTE
File Extension: PDF
File size: 3.23 MB
Pages: 184
Author: Saoussen Krichen, Jouhaina Chaouachi
ISBN: 9781848217430, 1848217439
Language: English
Year: 2014
Edition: 1

Product desciption

Graphrelated Optimization And Decision Theory 1st Edition Saoussen Krichen by Saoussen Krichen, Jouhaina Chaouachi 9781848217430, 1848217439 instant download after payment.

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.

Related Products