论文标题
港口基础架构计划的两个阶段随机优化模型
A Two Stage Stochastic Optimization Model for Port Infrastructure Planning
论文作者
论文摘要
本文研究了不确定的商品需求条件下的内陆港口基础设施投资计划。开发了两阶段的随机优化，以模拟需求不确定性对基础设施计划和运输决策的影响。两阶段的随机模型可最大程度地减少预期成本，包括与处理设备和存储相关的容量扩展投资成本以及预期的运输成本。为了解决该问题，实施了加速的弯曲器分解算法。 McCllean-Kerr Arkansas River Navigation System（Mkarns）的Arkansas部分用作模型的测试场。结果表明，随着港口基础设施投资的增加，商品量以及通过水道（以吨米为单位）移动的量的百分比增加。该模型能够确定在任何投资方案下应在港口容量中获得投资的端口集群。通过计算随机解决方案（VSS）的值来证明使用随机方法是合理的。
This paper investigates inland port infrastructure investment planning under uncertain commodity demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The two-stage stochastic model minimizes the total expected costs, including the capacity expansion investment costs associated with handling equipment and storage, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The Arkansas section of the McCllean-Kerr Arkansas River Navigation System (MKARNS) is used as a testing ground for the model. Results show that commodity volume and, as expected, the percent of that volume that moves via waterways (in ton-miles) increases with increasing investment in port infrastructure. The model is able to identify a cluster of ports that should receive investment in port capacity under any investment scenario. The use of a stochastic approach is justified by calculating the value of the stochastic solution (VSS).
