LEADER 05683nam 2200733 450 001 9910811182603321 005 20200903223051.0 010 $a1-118-98424-2 010 $a1-118-98426-9 010 $a1-118-98425-0 035 $a(CKB)3710000000239192 035 $a(EBL)1784143 035 $a(OCoLC)890981687 035 $a(SSID)ssj0001375499 035 $a(PQKBManifestationID)11746476 035 $a(PQKBTitleCode)TC0001375499 035 $a(PQKBWorkID)11360274 035 $a(PQKB)10296377 035 $a(MiAaPQ)EBC1784143 035 $a(Au-PeEL)EBL1784143 035 $a(CaPaEBR)ebr10930298 035 $a(CaONFJC)MIL646253 035 $a(PPN)189474521 035 $a(EXLCZ)993710000000239192 100 $a20140926h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aGraph-related optimization and decision support systems /$fSaoussen Krichen, Jouhaina Chaouachi 210 1$aLondon, England ;$aHoboken, New Jersey :$cISTE :$cWiley,$d2014. 210 4$dİ2014 215 $a1 online resource (186 p.) 225 1 $aFocus Computer Engineering Series,$x2051-249X 300 $aDescription based upon print version of record. 311 $a1-322-14998-4 311 $a1-84821-743-9 320 $aIncludes bibliographical references and index. 327 $aCover page; Half-Title page; Title page; Copyright page; Contents; List of Tables; List of Figures; List of Algorithms; Introduction; 1: Basic Concepts in Optimization and Graph Theory; 1.1. Introduction; 1.2. Notation; 1.3. Problem structure and variants; 1.4. Features of an optimization problem; 1.5. A didactic example; 1.6. Basic concepts in graph theory; 1.6.1. Degree of a graph; 1.6.2. Matrix representation of a graph; 1.6.3. Connected graphs; 1.6.4. Itineraries in a graph; 1.6.5. Tree graphs; 1.6.6. The bipartite graphs; 1.7. Conclusion; 2: Knapsack Problems; 2.1. Introduction 327 $a2.2. Graph modeling of the knapsack problem2.3. Notation; 2.4. 0-1 knapsack problem; 2.5. An example; 2.6. Multiple knapsack problem; 2.6.1. Mathematical model; 2.6.2. An example; 2.7. Multidimensional knapsack problem; 2.7.1. Mathematical model; 2.7.2. An example; 2.8. Quadratic knapsack problem; 2.8.1. Mathematical model; 2.8.2. An example; 2.9. Quadratic multidimensional knapsack problem; 2.9.1. Mathematical model; 2.9.2. An example; 2.10. Solution approaches for knapsack problems; 2.10.1. The greedy algorithm; 2.10.2. A genetic algorithm for the KP; 2.10.2.1. Solution encoding 327 $a2.10.2.2. Crossover2.10.2.3. Mutation; 2.11. Conclusion; 3: Packing Problems; 3.1. Introduction; 3.2. Graph modeling of the bin packing problem; 3.3. Notation; 3.4. Basic bin packing problem; 3.4.1. Mathematical modeling of the BPP; 3.4.2. An example; 3.5. Variable cost and size BPP; 3.5.1. Mathematical model; 3.5.2. An example; 3.6. Vector BPP; 3.6.1. Mathematical model; 3.6.2. An example; 3.7. BPP with conflicts; 3.7.1. Mathematical model; 3.7.2. An example; 3.8. Solution approaches for the BPP; 3.8.1. The next-fit strategy; 3.8.2. The first-fit strategy; 3.8.3. The best-fit strategy 327 $a3.8.4. The minimum bin slack3.8.5. The minimum bin slack'; 3.8.6. The least loaded heuristic; 3.8.7. A genetic algorithm for the bin packing problem; 3.8.7.1. Solution encoding; 3.8.7.2. Crossover; 3.8.7.3. Mutation; 3.9. Conclusion; 4: Assignment Problem; 4.1. Introduction; 4.2. Graph modeling of the assignment problem; 4.3. Notation; 4.4. Mathematical formulation of the basic AP; 4.4.1. An example; 4.5. Generalized assignment problem; 4.5.1. An example; 4.6. The generalized multiassignment problem; 4.6.1. An example; 4.7. Weighted assignment problem 327 $a4.8. Generalized quadratic assignment problem4.9. The bottleneck GAP; 4.10. The multilevel GAP; 4.11. The elastic GAP; 4.12. The multiresource GAP; 4.13. Solution approaches for solving the AP; 4.13.1. A greedy algorithm for the AP; 4.13.2. A genetic algorithm for the AP; 4.13.2.1. Solution encoding; 4.13.2.2. Crossover; 4.13.2.3. Mutation; 4.14. Conclusion; 5: The Resource Constrained Project Scheduling Problem; 5.1. Introduction; 5.2. Graph modeling of the RCPSP; 5.3. Notation; 5.4. Single-mode RCPSP; 5.4.1. Mathematical modeling of the SM-RCPSP; 5.4.2. An example of an SM-RCPSP 327 $a5.5. Multimode RCPSP 330 $a Constrained optimization is a challenging branch of operations research that aims to create a model which has a wide range of applications in the supply chain, telecommunications and medical fields. As the problem structure is split into two main components, the objective is to accomplish the feasible set framed by the system constraints. The aim of this book is expose optimization problems that can be expressed as graphs, by detailing, for each studied problem, the set of nodes and the set of edges. This graph modeling is an incentive for designing a platform that integrates all optimizatio 410 0$aFocus series in computer engineering. 606 $aConstrained optimization 606 $aDifferential equations, Partial 606 $aTelecommunication 615 0$aConstrained optimization. 615 0$aDifferential equations, Partial. 615 0$aTelecommunication. 676 $a519.6 686 $a90-01$a90C35$a90C27$a05C90$2msc 700 $aKrichen$b Saoussen$01616203 702 $aChaouachi$b Jouhaina 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910811182603321 996 $aGraph-related optimization and decision support systems$94040892 997 $aUNINA