This book is the first work to conduct the emergency logistics optimization problem under the epidemic environment (whether natural or man-made), which provides a new perspective for the application of optimization theory. In this book, the research methods involve epidemic dynamics, scenario-based emergency decision-making method, big data which combines the traditional and emerging technologies. The authors take epidemic outbreak as the research object and deeply integrate the epidemic spread model with the optimization model of emergency resource scheduling, which opens up a novel application area of operations research.

Chapter 1 Basic concept of epidemic-logistics
1.1 Basic knowledge of epidemic dynamics
1.1.1 Adequate contact rate and incidence
1.1.2 Basic reproduction number
1.2 Epidemics control and logistics operations
1.2.1 Preparedness
1.2.2 Outbreak investigation
1.2.3 Response
1.2.4 Evaluation
1.3 Future directions for epidemic-logistics research
References
Chapter 2 Epidemic dynamics modeling and analysis
2.1 Epidemic dynamics in anti-bioterrorism system
2.1.1 Introduction
2.1.2 SIQRS epidemic diffusion model
2.1.3 SEIQRS epidemic diffusion model
2.1.4 Computational experiments and result analysis
2.2 Epidemic dynamics modeling for influenza
2.2.1 Introduction
2.2.2 SEIRS model with small world network
2.2.3 Emergency demand base on epidemic diffusion model
2.2.4 Numerical test
2.3 Epidemic dynamics considering population migration
2.3.1 Introduction
2.3.2 Epidemic model with population migration
2.3.3 Model analysis
2.3.4 Numerical test
References
Chapter 3 Mixed distribution mode for emergency resources in anti-bioterrorism system
3.1 Introduction
3.2 Literature review
3.2.1 Literature related to epidemic prevention and control
3.2.2 Literature related to emergency distribution
3.3 Demand forecasting based on epidemic dynamics
3.3.1 SEIQRS model based on small-world network
3.3.2 Demand for emergency resources
3.4 Model formulations
3.4.1 Point-to-point distribution mode with no vehicle constraints
3.4.2 The multi-depot, multiple traveling salesmen distribution mode with vehicle constraints
3.4.3 The mixed-collaborative distribution mode
3.5 Solution procedures
3.5.1 Operating instructions for genetic algorithms
3.5.2 The solution procedure
3.6 Computational experiments and result analysis
3.6.1 Comparison and analysis for each stockpile depot
3.6.2 Comparison and analysis for total distance and timeliness
3.7 Conclusions
References
Chapter 4 Epidemic logistics with demand information updating-- Model I : Medical resource is enough
4.1 Introduction
4.2 Literature review
4.2.1 Epidemic diffusion modeling
4.2.2 Medical resource allocation modeling
4.3 The mathematical model
4.3.1 SEIRS epidemic diffusion model
4.3.2 The forecasting model for the time-varying demand
4.3.3 Time-space network of the medicine logistics
4.4 Solution methodology
4.5 Numerical tests
4.5.1 A numerical example
4.5.2 Model comparison
4.5.3 Sensitivity analysis
4.6 Conclusions
References
Chapter 5 Epidemic logistics with demand information updating---Model II : Medical resource is limited
5.1 Introduction
5.2 Epidemic diffusion analysis and demand forecasting
5.2.1 Influenza diffusion analysis
5.2.2 Demand forecasting
5.3 The dynamic medical resources allocation model
5.3.1 Model specification
5.3.2 Notation
5.3.3 Model formulation
5.3.4 Solution procedure
5.4 Numerical example and discussion
5.4.1 Numerical example
5.4.2 Comparison and discussion
5.4.3 A short sensitivity analysis
5.5 Conclusions
References
Chapter 6 Integrated optimization model for two-level epidemic-logistics network
6.1 Introduction
6.2 Problem description
6.2.1 SEIR epidemic diffusion model
6.2.2 Forecasting model for the time-varying demand
6.2.3 Forecasting model for the time-varying inventory
6.3 Optimization model and solution methodology
6.3.1 The integrated optimization model
6.3.2 Solution methodology
6.4 A numerical example and implications
6.4.1 A numerical example
6.4.2 A short sensitivity analysis
6.5 Conclusions
References
Chapter 7 Integrated optimization model for three-level epidemic-logistics network
7.1 Introd