A Study Of Modeling And Algorithms For Emergency Resource Allocation Under Uncertainty Environment

With the rapid development of science and technology, early warningcapabilities for all kinds of disasters have improved to some extent, but thedisasters still do great harm on human safety and stable development ofhuman social life. After the occurrence of unexpected events, therequirement of large-scale delivery for emergency relief supplies occurs.The effectiveness of emergency supplies delivery is a key element ofemergency management. Initial emergency response with the highlyuncertain information, the optimized allocation decision of a varietyemergency supplies for each affected point is very important, which is notonly necessary to save lives and property assurance, but also necessarywith the victims of emotional stability and avoiding social extreme events.Therefore, the study of allocation of decision optimization problem foremergency supplies is with great practical significance.With support of the National Science Foundation under, this articleaims to study the decisions-making optimization of emergency resourceallocation under uncertainty, to provide a reference for our emergencysupplies management policy. Firstly, existing research of emergencylogistics and emergency resource allocation are reviewed, we analyze theshortcomings and that we can learn from. Secondly, we study the theoriesof scenario analysis, fairness and multi-objective modeling and algorithmsfor supporting our modeling and algorithms. Thirdly, we build model forthe problem of emergency resource allocation with shortage and certaindemand called pull-model. We proposed a heuristic MOPSO algorithm tosolve the model. Fourthly, we build a model based on scenario for the emergency allocation problem under uncertain demand and resourceshortage called push-model, proposing algorithms based on heuristicMOPSO. Last, numeric result of pull-model and push-model based onWenchuan earthquake shows the effectiveness of our model andalgorithms.