Integer Optimization and its Computation in Emergency Management

298 Pages·2023·10.804 MB·English

by  Zhengtian Wu

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Integer Optimization and Its Computation in Emergency Management 
First edition 
Zhengtian Wu 
Suzhou University of Science and Technology, Suzhou, China 
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Table of Contents 

Cover image 

Title page 

Copyright 

Biography 

Dr Zhengtian Wu (1986–) 

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 lin- 
ear programming approach 

Abstract 

2.1. Converting the problem to a mixed 0–1 linear programming 

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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 ground- 
ings using a distributed fixed-point approach 

Abstract 

4.1. Introduction 

4.2. Problem formulation 

4.3. Methodology 

4.4. Computational experience 

4.5. Conclusion 

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References 

Chapter 5: Solving multiple fleet airline disruption problems using a distrib- 
uted-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 min- 
imum 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 

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Chapter 7: An approximation algorithm for graph partitioning via deter- 
ministic 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 knap- 
sack 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 

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References 

Index 
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Biography 
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