The Humanistic Rescue Resource Allocation Under Uncertain Time

Large-scale natural disasters always lead to large-scale casualties and economic losses.Humanitarian logistics have attracted more and more attention from peoples,especially scholars and related practitioners.After the disaster,humanitarian relief supplies were sent from the emergency rescue center to the affected areas in various ways.The difficult prediction of the type and the intensity of disasters and the destruction of the infrastructure and road traffic network will lead to the incompletion of the rescue information.Therefore,base on two situations that lead to time uncertainty,this paper studies the problem of post-disaster relief material dispatching.First of all,this paper studies the problem of multi-rescue center humanitarian relief equipment dispatching under the road congestion situation.Considering the uncertainty of the rescue environment,the problem of material distribution between the emergency rescue center and the disaster site is studied,and the corresponding multi-center multi-material network flow model is established.To solve the uncertainty of the rescue environment,this paper simplifies the model by using the gray evaluation method of the triangular whitening weight function,selecting the most reliable path from the distribution center to the disaster point,and making all the vehicles only take the most reliable path,which can reduce the amount of calculation.Secondly,considering the damage of the basic traffic network after the disaster will lead to the uncertainty of the running time of the vehicle on the road,this paper continues to study the scheduling problem of the relief material under the uncertain driving time.Based on the uncertainties of the time when the vehicle is on the road,the uncertain optimization model satisfying the demand of the disaster area in the golden rescue time is established.In order to solve this model,we use the gray assessment method to determine the most reliable path from the rescue center to the disaster point,which can simplify the original model.However,since the stochastic factor of vehicle travel time is not eliminated,this paper further uses the robust optimization to transform the simplified model.The transformed robust optimization model is a deterministic mixed integer programming model,which is easy to solve and finally obtained.It can guarantee the vehicles arrive at the disaster area before time T with the probability of 1-α.Finally,in order to verify the feasibility and robustness of the model,this paper carries out numerical experiments.Through a series of sensitivity analysis,we can answer a series of questions:(1)how the degree of the uncertainty of driving time on the road impact the solution;(2)how the probability of arriving at the affected area on time impact the solution;(3)how the size of the problem influences the solution;(4)how the number and the kind of vehicles impact the solution;(5)how the departure interval influences the solution.