时间不确定情形下人道主义救援物资调度问题研究

发布时间：2018-06-29 01:46

本文选题：人道主义救援 + 物资调度　；参考：《山东大学》2017年硕士论文

【摘要】：大规模自然灾难大多导致了大量的人员伤亡和经济损失,人道主义物流越来越受到人们的关注——尤其是学者和相关从业人员。灾害发生后,人道主义救援物资通过各种方式从应急救援中心送往受灾点。由于发生灾难的类型、强度难以预测,以及灾害发生后基础设施、道路交通网络的不确定,这些均会导致救援过程中的信息不完全。因此,本文考虑了两种导致时间不确定的情形——车辆堵塞与道路损毁,研究了灾后救援物资调度问题。首先,本文研究了道路拥挤情形下的多救援中心单受灾点的人道主义救援物资调度问题。考虑救援环境的不确定性,本文研究了灾后人道主义救援物资的配送问题,建立了相应的多中心多物资网络流模型。本文运用三角白化权函数灰色评估方法来处理救援环境的不确定性,选出从配送中心到受灾点的最可靠路径,并作出所有车辆都只走最可靠路径的假设,从而将模型进行了简化,降低了计算量。其次,考虑到灾后基础交通网络受损会导致车辆在道路上行驶时间不确定,本文进一步研究道路行驶时间不确定情形下救援物资的调度问题。基于救援中心的车辆错时发车以及车辆在道路上的行驶时间的不确定,本文建立了在黄金救援时间内满足灾区需求的不确定优化模型。为了解决救援过程中的信息不完全情况,我们通过灰色评估的方法确定各救援中心到受灾点的最可靠路径,从而将原模型进行了简化。但是,由于车辆行驶时间这个随机因素并未消除,本文进一步利用鲁棒优化对简化后的模型的进行转化。转换后的鲁棒优化模型是一个确定性的混合整数规划模型,易于求解,最终得到的解可以保证所有救援车辆至少以1-α的概率在T时间内到达灾区。最后,为了验证模型的可行性与鲁棒性,本文进行了数值实验,通过灵敏度分析实验回答了一系列的问题:(1)车辆在道路上行驶时间不确定车程度对解的影响;(2)准时到达概率1-α对解的影响;(3)问题规模对解的影响;(4)救援中心车辆数以及车辆类型对解的影响;(5)发车间隔对解的影响。
[Abstract]:Large scale natural disasters have resulted in a large number of casualties and economic losses, and humanitarian logistics has attracted more and more attention-especially scholars and related practitioners. After the disaster, humanitarian aid through various ways from the emergency relief center to the disaster site. Because of the type of disaster, the unpredictable intensity, the uncertainty of the infrastructure and the road traffic network, all of these will lead to incomplete information in the rescue process. Therefore, this paper considers two kinds of cases that lead to uncertain time-vehicle jam and road damage, and studies the problem of post-disaster relief material scheduling. First of all, this paper studies the problem of humanitarian relief material scheduling in single disaster site of multiple rescue centers under congested road conditions. Considering the uncertainty of rescue environment, this paper studies the distribution of humanitarian relief materials after disaster, and establishes the corresponding multi-center and multi-material network flow model. In this paper, the grey evaluation method of triangular whitening weight function is used to deal with the uncertainty of the rescue environment, and the most reliable route from the distribution center to the disaster spot is selected, and the assumption that all vehicles only take the most reliable path is made. Thus, the model is simplified and the calculation amount is reduced. Secondly, considering that the damage to the basic traffic network after the disaster will lead to the uncertainty of vehicle travel time on the road, this paper further studies the problem of the dispatch of relief materials under the uncertain road travel time. Based on the uncertain travel time of the vehicles in the rescue center and the uncertainty of the travel time of the vehicles on the road, an uncertain optimization model is established to meet the needs of the disaster areas during the gold rescue time. In order to solve the incomplete information situation in the rescue process, the most reliable path from each rescue center to the disaster site is determined by grey evaluation method, and the original model is simplified. However, since the random factor of vehicle travel time is not eliminated, the simplified model is further transformed by robust optimization. The transformed robust optimization model is a deterministic mixed integer programming model, which is easy to solve. The final solution can ensure that all rescue vehicles reach the disaster area in T time at least with the probability of 1- 伪. Finally, in order to verify the feasibility and robustness of the model, numerical experiments are carried out in this paper. Through sensitivity analysis experiment, a series of questions are answered: (1) the influence of uncertain vehicle time on road, (2) the influence of punctual arrival probability 1- 伪 on solution, (3) the effect of problem size on solution, and (4) the influence of vehicle in rescue center. The influence of number and vehicle type on the solution; (5) the effect of departure interval on the solution.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：D632.5

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