| 疫情物流建模:运筹学的新视角:a new perspective on operations research |
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2020-06-21 00:00:00 |
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疫情物流建模:运筹学的新视角:a new perspective on operations research 本书特色
本书将从突发疫情环境下的应急物流网络优化与常规疫情环境下的药品物流调度两个视角，探讨医疗物资调度的优化理论与方法。在应急环境下，本书将结合生物恐怖袭击这一非常规突发事件的应急救援活动，开展基于生物危险源扩散模型的应急救援控制策略研究、时间驱动环境下应急物资混合协同配送方法研究、资源驱动环境下应急物流网络协同优化研究、生物反恐体系中应急物流网络集成动态优化研究以及应急救援方案选择的序贯决策方法研究。
疫情物流建模:运筹学的新视角:a new perspective on operations research 目录
ContentsChapter 1 Basic concept of epidemic-logistics 11.1 Basic knowledge of epidemic dynamics 11.1.1 Adequate contact rate and incidence 21.1.2 Basic reproduction number 31.2 Epidemics control and logistics operations 41.2.1 Preparedness 41.2.2 Outbreak investigation 51.2.3 Response 61.2.4 Evaluation 71.3 Future directions for epidemic-logistics research 8References 10Chapter 2 Epidemic dynamics modeling and analysis 132.1 Epidemic dynamics in anti-bioterrorism system 132.1.1 Introduction 132.1.2 SIQRS epidemic diffusion model 152.1.3 SEIQRS epidemic diffusion model 192.1.4 Computational experiments and result analysis 212.2 Epidemic dynamics modeling for influenza 242.2.1 Introduction 242.2.2 SEIRS model with small world network 252.2.3 Emergency demand base on epidemic diffusion model 292.2.4 Numerical test 302.3 Epidemic dynamics considering population migration 332.3.1 Introduction 332.3.2 Epidemic model with population migration 342.3.3 Model analysis 352.3.4 Numerical test 40References 43Chapter 3 Mixed distribution mode for emergency resources in anti-bioterrorism system 473.1 Introduction 473.2 Literature review 483.2.1 Literature related to epidemic prevention and control 493.2.2 Literature related to emergency distribution 503.3 Demand forecasting based on epidemic dynamics 513.3.1 SEIQRS model based on small-world network 513.3.2 Demand for emergency resources 523.4 Model formulations 533.4.1 Point-to-point distribution mode with no vehicle constraints 533.4.2 The multi-depot， multiple traveling salesmen distribution mode with vehicle constraints 543.4.3 The mixed-collaborative distribution mode 563.5 Solution procedures 583.5.1 Operating instructions for genetic algorithms 583.5.2 The solution procedure 593.6 Computational experiments and result analysis 603.6.1 Comparison and analysis for each stockpile depot 613.6.2 Comparison and analysis for total distance and timeliness 633.7 Conclusions 64References 64Chapter 4 Epidemic logistics with demand information updating—ModelⅠ: Medical resource is enough 674.1 Introduction 674.2 Literature review 684.2.1 Epidemic diffusion modeling 684.2.2 Medical resource allocation modeling 694.3 The mathematical model 704.3.1 SEIRS epidemic diffusion model 714.3.2 The forecasting model for the time-varying demand 734.3.3 Time-space network of the medicine logistics 744.4 Solution methodology 774.5 Numerical tests 784.5.1 A numerical example 784.5.2 Model comparison 814.5.3 Sensitivity analysis 834.6 Conclusions 84References 85Chapter 5 Epidemic logistics with demand information updating—ModelⅡ: Medical resource is limited 885.1 Introduction 885.2 Epidemic diffusion analysis and demand forecasting 915.2.1 Influenza diffusion analysis 915.2.2 Demand forecasting 935.3 The dynamic medical resources allocation model 955.3.1 Model specification 955.3.2 Notation 965.3.3 Model formulation 965.3.4 Solution procedure 975.4 Numerical example and discussion 975.4.1 Numerical example 975.4.2 Comparison and discussion 1005.4.3 A short sensitivity analysis 1025.5 Conclusions 102References 103Chapter 6 Integrated optimization model for two-level epidemic-logistics network 1066.1 Introduction 1066.2 Problem description 1076.2.1 SEIR epidemic diffusion model 1086.2.2 Forecasting model for the time-varying demand 1096.2.3 Forecasting model for the time-varying inventory 1116.3 Optimization model and solution methodology 1116.3.1 The integrated optimization model 1116.3.2 Solution methodology 1136.4 A numerical example and implications 1176.4.1 A numerical example 1176.4.2 A short sensitivity analysis 1226.5 Conclusions 123References 124Chapter 7 Integrated optimization model for three-level epidemic-logistics network 1257.1 Introduction 1257.2 Problem description 1277.2.1 Model framework 1277.2.2 Time-varying forecasting method for the dynamic demand 1287.2.3 Dynamic demand and inventory for the UHD 1297.3 Optimization model and solution procedure 1297.3.1 Optimization model 1297.3.2 Solution procedure 1317.4 Numerical example 1327.5 Conclusions 136References 137Chapter 8 A novel FPEA model for medical resources allocation in an epidemic control 1398.1 Introduction 1398.2 The mathematical model 1418.2.1 Forecasting phase 1428.2.2 Planning phase 1448.2.3 Execution phase 1508.2.4 Loop closed 1508.3 Numerical example 1528.3.1 Test for forecasting phase 1528.3.2 Test for logistic planning phase 1538.3.3 Test for adjustment phase 1578.4 Conclusions 159References 159Chapter 9 Integrated planning for public health
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