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Optimization Based Method for Supply Location Selection and Routing in Large-Scale Emergency Material Delivery

机译:基于优化的大规模应急物资供应地点选择与选路方法

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摘要

Timely supply of vital materials to disaster hit areas plays a critical role in emergency relief. The problem involves warehouse selection, fleet routing, and scheduling so as to meet demand in the strict time window. The problem is NP-hard, in general, and extremely difficult to solve. The congestion caused by heavy traffic further aggravates the problem. To obtain a scalable solution, a new method based on successive subproblem solving in Lagrangian Relaxation (LR) framework is developed. The route capacity and location selection constraints are relaxed by Lagrange multipliers, and the problem is converted into a two-level optimization problem. The subproblems at the lower level are solved successively in dual iterations with convergence assurance so that the indecomposable location constraints can be incorporated. A systematic method is developed to obtain a feasible solution by adding the once relaxed constraints back into the dual problem successively in feasibility iterations. Convergence proof of the new method and its properties are presented. Numerical results show that the new method is effective and efficient, and can be applied to large-scale problems.
机译:及时向受灾地区提供重要物资在紧急救济中起着至关重要的作用。问题涉及仓库的选择,车队的路线安排和调度,以便在严格的时间窗口内满足需求。通常,这个问题很难解决,并且非常难以解决。交通繁忙造成的交通拥堵进一步加剧了这个问题。为了获得可扩展的解决方案,开发了一种基于拉格朗日松弛(LR)框架中连续子问题求解的新方法。拉格朗日乘子放宽了路线的容量和位置选择的约束,该问题转化为两级优化问题。较低级别的子问题可以在具有收敛性保证的双重迭代中连续求解,以便可以合并不可分解的位置约束。通过在可行性迭代中将曾经放松的约束依次添加回对偶问题,开发了一种系统的方法来获得可行的解决方案。提出了新方法及其性质的收敛性证明。数值结果表明,该方法是有效且有效的,可应用于大规模问题。

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