Studies on integer optimization in emergency management have attracted engineers and scientists from various disciplines such as management, mathematics, computer science, and other fields. Although there are a large number of literature reports on integer planning and emergency events, few books systematically explain the combination of the two. Researchers need a clear and thorough presentation of the theory and application of integer programming methods for emergency management.
Integer Optimization and its Computation in Emergency Management investigates the computation theory of integer optimization, developing integer programming methods for emergency management and explores related practical applications. Pursuing a holistic approach, this book establishes a fundamental framework for this topic, intended for graduate students who are interested in operations research and optimization, researchers investigating emergency management, and algorithm design engineers working on integer programming or other optimization applications.
1. Integer Optimization and Its Computation in Emergency Management - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page v 7. Biography - p. Page v 8. List of Illustrations - p. Page vii 9. List of Tables - p. Page viii 10. Chapter 1 : Distributed implementation of the fixed-point method for integer optimization in emergency management - p. Page viii 11. Chapter 2 : Computing all pure-strategy Nash equilibria using mixed 0–1 linear programming approach - p. Page 10 12. Chapter 3 : Computing all mixed-strategy Nash equilibria using mixed integer linear programming approach - p. Page 25 13. Chapter 4 : Solving long-haul airline disruption problem caused by groundings using a distributed fixed-point approach - p. Page 35 14. Chapter 5 : Solving multiple fleet airline disruption problems using a distributed-computation approach - p. Page 83 15. Chapter 6 : A deterministic annealing neural network algorithm for the minimum concave cost transportation problem - p. Page 115 16. Chapter 7 : An approximation algorithm for graph partitioning via deterministic annealing neural network - p. Page 145 17. Chapter 8 : A logarithmic descent direction algorithm for the quadratic knapsack problem - p. Page 170 18. Index - p. Page 191
Studies on integer optimization in emergency management have attracted engineers and scientists from various disciplines such as management, mathematics, computer science, and other fields. Although there are a large number of literature reports on integer planning and emergency events, few books systematically explain the combination of the two. Researchers need a clear and thorough presentation of the theory and application of integer programming methods for emergency management.
Integer Optimization and its Computation in Emergency Management investigates the computation theory of integer optimization, developing integer programming methods for emergency management and explores related practical applications. Pursuing a holistic approach, this book establishes a fundamental framework for this topic, intended for graduate students who are interested in operations research and optimization, researchers investigating emergency management, and algorithm design engineers working on integer programming or other optimization applications.
1. Integer Optimization and Its Computation in Emergency Management - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page v 7. Biography - p. Page v 8. List of Illustrations - p. Page vii 9. List of Tables - p. Page viii 10. Chapter 1 : Distributed implementation of the fixed-point method for integer optimization in emergency management - p. Page viii 11. Chapter 2 : Computing all pure-strategy Nash equilibria using mixed 0–1 linear programming approach - p. Page 10 12. Chapter 3 : Computing all mixed-strategy Nash equilibria using mixed integer linear programming approach - p. Page 25 13. Chapter 4 : Solving long-haul airline disruption problem caused by groundings using a distributed fixed-point approach - p. Page 35 14. Chapter 5 : Solving multiple fleet airline disruption problems using a distributed-computation approach - p. Page 83 15. Chapter 6 : A deterministic annealing neural network algorithm for the minimum concave cost transportation problem - p. Page 115 16. Chapter 7 : An approximation algorithm for graph partitioning via deterministic annealing neural network - p. Page 145 17. Chapter 8 : A logarithmic descent direction algorithm for the quadratic knapsack problem - p. Page 170 18. Index - p. Page 191
