An Accelerated Solution Method for Two-Stage Stochastic Models in Disaster Management (Mathematische Optimierung und Wirtschaftsmathematik | Mathematical Optimization and Economathematics)

Emilia Graß develops a solution method which can provide fast and near-optimal solutions to realistic large-scale two-stage stochastic problems in disaster management. The author proposes a specialized interior-point method to accelerate the standard L-shaped algorithm. She shows that the newly developed solution method solves two realistic large-scale case studies for the hurricane prone Gulf and Atlantic coast faster than the standard L-shaped method and a commercial solver. The accelerated solution method enables relief organizations to employ appropriate preparation measures even in the case of short-term disaster warnings. Contents Quantitative Optimization Models in Disaster Management: A Literature Review Solution Methods in Disaster Management: A Literature Review The Accelerated L-Shaped Method Case Study Design Numerical Experiments and Analysis Target Groups Scientist and students in the fields of operations research, optimization and numerical algorithms Practitioners working in charities and NGOs About the Author Emilia Graß holds a PhD from the Hamburg University of Technology, Germany. She is currently working as guest researcher on the project cyber security in healthcare at the Centre for Health Policy, Imperial College London, UK. Her scientific focus is on stochastic programming, solution methods, disaster management and healthcare.-- Provided by publisher Front Matter ....Pages I-XVII Introduction (Emilia Graß)....Pages 1-2 Two-Stage Stochastic Programs for Pre-Positioning Problems in Disaster Management (Emilia Graß)....Pages 3-35 Solution Algorithms in Disaster Management (Emilia Graß)....Pages 37-69 Numerical Experiments (Emilia Graß)....Pages 71-106 Conclusion (Emilia Graß)....Pages 107-109 Back Matter ....Pages 111-155 Emilia Grass develops a solution method which can provide fast and near-optimal solutions to realistic large-scale two-stage stochastic problems in disaster management.