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. Investigates computation theory of integer optimization and integer programming methods for emergency management and related practical applications Systematically provides background and potential applications of integer programming in emergency events, providing specific calculation frameworks and examples Provides a clear and thorough presentation of the theory and application of integer programming methods for emergency management through a holistic approach, establishing a fundamental framework of the topic for the audience
Author(s): Zhengtian Wu
Series: Emerging Methodologies and Applications in Modelling, Identification and Control
Edition: 1
Publisher: Elsevier
Year: 2023
Language: English
Pages: 214
Chapter 1: Distributed implementation of the fixed-point method for integer
optimization in emergency management
Abstract
1.1. Dang and Ye's fixed-point iterative method
1.2. Some details of the distributed implementation
1.3. The computation of the market split problem
1.4. The computation of the knapsack feasibility problem
1.5. Summary
References
Chapter 2: Computing all pure-strategy Nash equilibria using mixed 0–1 linear
programming approach
Abstract
2.1. Converting the problem to a mixed 0–1 linear programming
3
2.2. Numerical results
2.3. Summary
References
Chapter 3: Computing all mixed-strategy Nash equilibria using mixed integer
linear programming approach
Abstract
3.1. Converting the problem to a mixed integer linear programming
3.2. Numerical results
3.3. Summary
References
Chapter 4: Solving long-haul airline disruption problem caused by groundings
using a distributed fixed-point approach
Abstract
4.1. Introduction
4.2. Problem formulation
4.3. Methodology
4.4. Computational experience
4.5. Conclusion
4
References
Chapter 5: Solving multiple fleet airline disruption problems using a distributed-
compu-tation approach
Abstract
5.1. Problem formulation
5.2. Methodology
5.3. Computational experience
5.4. Conclusion
References
Chapter 6: A deterministic annealing neural network algorithm for the minimum
concave cost transportation problem
Abstract
6.1. Introduction
6.2. Deterministic annealing and neural networks
6.3. Stability and convergence analysis of the proposed algorithm
6.4. Numerical results
6.5. Conclusions
References
5
Chapter 7: An approximation algorithm for graph partitioning via deterministic
annealing neural network
Abstract
7.1. Introduction
7.2. Entropy-type Barrier function
7.3. DANN algorithm for graph partitioning
7.4. Proof of global convergence of iterative procedure
7.5. Numerical results
7.6. Conclusions
References
Chapter 8: A logarithmic descent direction algorithm for the quadratic knapsack
problem
Abstract
8.1. Introduction
8.2. Logarithmic descent direction algorithm
8.3. Convergence of the damped Newton method
8.4. Numerical results
8.5. Conclusions
6
References
Index
