Robust Optimization Of Two Types Of Emergency Vehicle Routing Problem With Incomplete Information

In recent years, many sudden natural disasters and public health events occur frequently, which cause huge losses not only in economics but also in safety to humans. Therefore, after the occurrence of unexpected events, it is extremely important to study the transportation problem of emergency supplies in disaster relief activities. Due to unexpected events with uncertainty, this characteristic determines the model with uncertain time and uncertain requirement. According of the above, the assay study the vehicle routing problem in emergency logistics with incomplete information after the occurrence of unexpected events.Combined the characteristic of the emergency logistics, the essay study the vehicle routing problem. For the vital role of the time effectiveness in emergency management, meanwhile, because many infrastructure and communications equipment are damaged, which cause the uncertainty of road information and disaster area, the thesis discussed the vehicle routing problem with uncertain time and the vehicle routing problem with not only uncertain time but also uncertain requirement. Then we use robust discrete optimization to address data uncertainty of the vehicle routing problem, and obtain the robust counterpart. We also showed that the robust counterpart can be solved by solving linear optimization, which greatly reduces the computational complexity. And the robust discrete optimization approach we used allows controlling the degree of conservatism of the solution. Finally, we use an example to prove the analysis and the algorithm are computationally tractable.