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A stochastic programming model for emergency supply planning considering transportation network mitigation and traffic congestion
Socio-Economic Planning Sciences ( IF 6.1 ) Pub Date : 2021-07-02 , DOI: 10.1016/j.seps.2021.101119
Qingyi Wang 1 , Xiaofeng Nie 2, 3
Affiliation  

How to conduct effective and efficient emergency supply planning is a challenging task. In this paper, we tackle a general emergency supply planning problem. The problem not only integrates the decisions of transportation network mitigation and emergency supply pre-positioning before disasters, but also considers post-disaster dynamic transportation planning with traffic congestion effects incorporated. We formulate this problem as a two-stage stochastic programming model, which aims to minimize the expected total cost related to various disaster mitigation, preparedness, and response decisions. A variant of the model is optimally solved by applying a generalized Benders decomposition algorithm, which significantly outperforms state-of-the-art global optimization solvers. Finally, a case study for a hurricane threat in the southeastern U.S. is conducted to demonstrate the advantages of our model and to illustrate insights on the optimal network mitigation and pre-positioning plan as well as the transportation plan. It is shown that considering traffic congestion effects and dynamic transportation plans brings about spatial and temporal flexibility for achieving better emergency supply plans.



中文翻译:

考虑交通网络缓解和交通拥堵的应急供应规划随机规划模型

如何进行有效和高效的应急供应计划是一项具有挑战性的任务。在本文中,我们解决了一个一般的应急供应计划问题。该问题不仅综合了灾前交通网络缓解和应急供应预置的决策,而且还考虑了灾后动态交通规划,并结合了交通拥堵效应。我们将此问题制定为两阶段随机规划模型,旨在最小化与各种减灾、备灾和响应决策相关的预期总成本。通过应用广义 Benders 分解算法对模型的一个变体进行了优化求解,该算法显着优于最先进的全局优化求解器。最后,美国东南部飓风威胁的案例研究 进行是为了展示我们模型的优势,并说明对最佳网络缓解和预定位计划以及运输计划的见解。结果表明,考虑交通拥堵效应和动态交通计划带来了空间和时间上的灵活性,以实现更好的应急供应计划。

更新日期:2021-07-02
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