Scheduling Research Of Emergency Roadway Repair And Relief Material Distribution After An Earthquake

Almost all the roads were destroyed and cut off by the earthquake, which lead to the difficulty in relief distribution and made the people trapped inside the disaster zone. The blocked roads must be repaired immediately to make sure the relief materials delivered to every survivor's shelter on time, which will reduce the casualty and property lost. However, emergency roadway repairing and relief distribution planning following an earthquake have traditionally been done manually and separately, based on the decision-makers experience. And the roadway repair was frequently put into higher priority. These decisions always disregard the interrelationship between two tasks. Therefore, a simultaneous decision-making technique should be a more efficient way, namely, planning the relief distribution routes base on the emergency repair process, and make appropriate adjustment during the process, and vice versa. This method will increase the rescue efficiency dramatically and then minimize the time of relief distribution and the casualty.In this paper, after reviewing relevant literatures regardless domestics or foreign,we construed two time-space networks for both emergency repair and relief distribution using the time-space flow technique. In considering the interrelationship between the emergency repair and relief distribution, a multi-objective, mixed-integer, multiple-commodity network model was built. The paper discussed two different cases, firstly, only one team working in a desired point, and secondly, two teams working from both sides of the destroyed road. Relevant data was collected based on the real impact of "5.12" Wenchuan quake on the roads in Shifang city, reasonable assumptions were made, and these two cases problem models were solved by the mathematical software "Lingo", then we obtained the time needs for repairing desired points and repairing schedule, also the time needs for relief distribution to different demand points and delivery route. We performed sensitivity analysis on these two key parameters in the model, target weight, and the work team scale, and obtained the optimized work team quantity. Finally, comparison of results of two different cases shows that the second case is a better option, because the second case can increase space to manage the work team, which leads to effective usage of limited repairing resource, eventually save the time for both repairing and distribution, and improve the rescue efficiency.