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Graph-related optimization and decision theory /
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 feasib...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Wiley,
2014.
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| Colección: | ISTE.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover 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.
- 2.2. 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.
- 2.10.2.2. 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.
- 3.8.4. 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.
- 4.8. 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.