# Graph-related optimization and decision support systems / Saoussen Krichen, Jouhaina Chaouachi.

Material type: TextSeries: Focus series in computer engineeringPublisher: London : Hoboken, NJ : ISTE, Ltd. ; Wiley, 2014Description: 1 online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781118984260; 1118984269; 9781118984253; 1118984250Subject(s): Graph theory | Mathematical optimization | Decision support systems | Decision support systems | Graph theory | Mathematical optimization | COMPUTERS / Information TechnologyGenre/Form: Electronic books.Additional physical formats: Print version:: Graph-related optimization and decision support systems.DDC classification: 511/.5 LOC classification: QA166Online resources: Wiley Online LibraryBasic Concepts in Optimization and Graph Theory / Saoussen Krichen, Jouhaina Chaouachi -- Knapsack Problems / Saoussen Krichen, Jouhaina Chaouachi -- Packing Problems / Saoussen Krichen, Jouhaina Chaouachi -- Assignment Problem / Saoussen Krichen, Jouhaina Chaouachi -- The Resource Constrained Project Scheduling Problem / Saoussen Krichen, Jouhaina Chaouachi -- Spanning Tree Problems / Saoussen Krichen, Jouhaina Chaouachi -- Steiner Problems / Saoussen Krichen, Jouhaina Chaouachi -- A DSS Design for Optimization Problems / Saoussen Krichen, Jouhaina Chaouachi.

Includes bibliographical references and index.

Online resource; title from PDF title page (John Wiley, viewed Oct. 7, 2014).

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.