Study On Model And Algorithm Of CLRIP Facing Unconventional Emergencies

It is still very difficult to forecast the occurrence of large-scale unconventional emergencies by virtue of present science and technology. Effective decision mechanism and logistics network system shoud be established to minimize the loss of life and property in the affected areas after unconventional emergencies. CLRIP (Combined Location Routing and Inventory Problem), which solves facility location, routing determination and inventory control systematically and has been hotpot of research on logestics system at home and abroad, provides theory guidance and practice reference for us. However, the research on CLRIP on the background of unconventional emergencies, especially for emergency CLRIP facing conflicting multi-objective, has been rarely seen presentyl. This paper commits to the research on decision model of conflicting multi-objective emergency CLRIP, and concrete contents are as follows:(1) The evaluation of affected degree. On the condition of limited resource supply, the areas affected more seriously should be given a certain priority for resource attaining. So it’s necessary to evaluate the affected degree of different areas. This paper adopts TOPSIS as the evaluation method based on establishing theevaluation index system.(2) The model of emergency vehicle path. The difference between emergency vehicle path problem and conventional path problem attributes to the feature of emergency aid and demand characteristic of the affected areas. A model of emergency vehicle path aiming at minimizing the distribution time of the system is established in this paper.(3) The establishment of multi-objective emergency CLRIP model and correspongding algorithm. Based on the work of the three chapters before, a model of emergency CLRIP with conflicting multi-objective is proposed, taking the total time and cost of the logistics system and the affected degree of demanding points into consideration. The model could figure out problems of emergency facility location, emergency vehicle path and emergency resource allocation systematically. To solve the model, a genetic and simulated annealing algorithm (GASA) is designed.(4) Finally, this paper takes data from large-scale disaster in reality using Matlab7.0programmer to verify feasibility and effectiveness of the model and algorithm.CLRIP on the background of unconventional emergencies is studied systematically in this paper, which not only breaks through the bottleneck of previous studies, but also provides guidance for the practice of emergency management.