內容大鋼
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.
目錄
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 Introduction
7.2 Problem description
7.2.1 Model framework
7.2.2 Time-varying forecasting method for the dynamic demand
7.2.3 Dynamic demand and inventory for the UHD
7.3 Optimization model and solution procedure
7.3.1 Optimization model
7.3.2 Solution procedure
7.4 Numerical example
7.5 Conclusions
References
Chapter 8 A novel FPEA model for medical resources allocation in an epidemic control
8.1 Introduction
8.2 The mathematical model
8.2.1 Forecasting phase
8.2.2 Planning phase
8.2.3 Execution phase
8.2.4 Loop closed
8.3 Numerical example
8.3.1 Test for forecasting phase
8.3.2 Test for logistic planning phase
8.3.3 Test for adjustment phase
8.4 Conclusions
References
Chapter 9 Integrated planning for public health emergencies: A modified model for controlling HIN1 pandemic
9.1 Introduction
9.2 Literature review
9.3 Model formulation
9.3.1 Epidemic compartmental model
9.3.2 Resource allocation model
9.3.3 Model solution
9.4 Case study
9.4.1 Background and parameters setting
9.4.2 Test results
9.4.3 Discussion
9.5 Conclusions
References
Chapter 10 Logistics planning for hospital pharmacy trusteeship under a hybrid of uncertainties
10.1 Introduction
10.2 Literature review
10.2.1 VMI in hospital
10.2.2 Logistics planning with different influence factors
10.3 Time-space network model
10.3.1 Network structure
10.3.2 Deterministic planning model
10.3.3 Stochastic planning model
10.4 Solution algorithms and evaluation methods
10.4.1 Solution method for DPM
10.4.2 Solution method for SPM
10.4.3 Evaluation method
10.5 Numerical tests
10.5.1 Data setting
10.5.2 Test results
10.5.3 Sensitivity analysis
10.6 Conclusions
References
Chapter 11 Medical resources order and shipment in community health service centers
11.1 Introduction
11.2 Literature review
11.3 Modeling approach
11.3.1 Network structure
11.3.2 The deterministic model (DM)
11.3.3 The stochastic model (SM)
11.4 Solution procedure and evaluation method
11.4.1 Solution procedure
11.4.2 Evaluation method
11.5 Numerical tests
11.5.1 Parameters setting
11.5.2 Test results
11.5.3 Sensitivity analysis
11.6 Conclusions
References
Chapter 12 Three short time-space network models for medicine management
12.1 Model I : A basic time-space network model
12.1.1 Introduction
12.1.2 The time-space network model
12.1.3 Solution algorithm
12.1.4 Numerical tests
12.1.5 Conclusions
12.2 Model II : An improved time-space network model
12.2.1 Introduction
12.2.2 Model formulation
12.2.3 The solution procedure
12.2.4 Numerical tests
12.2.5 Conclusions
12.3 Model III: A chance-constrained programming model based on time-space network
12.3.1 Introduction
12.3.2 Model formulation
12.3.3 The solution procedure
12.3.4 Numerical tests
12.3.5 Conclusions
References
Chapter 13 Epidemic-logistics network considering time windows and service level
13.1 Emergency materials distribution with time windows
13.1.1 Introduction
13.1.2 SIR epidemic model
13.1.3 Emergency materials distribution network with time windows
13.1.4 Numerical tests
13.1.5 Discussion
13.1.6 Conclusions
13.2 An improved location-allocation model for emergency logistics network design
13.2.1 Introduction
13.2.2 Model formulation
13.2.3 Solution procedure
13.2.4 Numerical test
13.2.5 Conclusions
References
Appendix A
Appendix B
B1 Model validation
B2 Optimization results with different budget sizes
B3 Impact of different intervention starting dates
Reference
[