Sensitivity Analysis of the Dynamic Fleet Management Problem with Applications in Fleet-Sizing, Pricing and Terminal Capacity Planning

动态车队管理问题的敏感性分析及其在车队规模、定价和码头容量规划中的应用

• 批准号: 0422133
• 负责人: Huseyin Topaloglu
• 金额: --
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-12-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0422133&HistoricalAwards=false
• 关键词: Sensitivity; Analysis; Dynamic; Fleet; Management

This grant provides funding for the development of algorithms that assess how the performance measures of a fleet management model would change, if a model parameter, such as fleet size, load availability or terminal capacity, were modified. The primary objective of the fleet management models is to make vehicle-to-load-assignment and vehicle-repositioning decisions, so that a performance measure, such as profit, deadhead miles or number of served loads, is optimized. However, a question that is commonly overlooked by these models is how the performance measure in question would change in response to changes in certain model parameters. For example, freight carriers are interested in how much their profits would increase if they introduce an additional vehicle into the system or if they serve an additional load. Railroad companies want to estimate the minimum number of railcars necessary to cover random shipper demands. Ideas from approximate dynamic programming, infinitesimal perturbation analysis and combinatorial optimization will be utilized to build these "sensitivity analysis" algorithms, which will subsequently be used to make tactical decisions, such as fleet-sizing, pricing and terminal-capacity planning.By and large, freight carriers use separate and uncoordinated models for their tactical and operational decisions. This research will help them to coordinate their tactical and operational decisions better. Currently, sensitivity analyses are carried out by time-consuming methods that involve "physically" adjusting the parameters and rerunning the models. The developed sensitivity analysis algorithms will quickly point out the critical parameters to the decision makers, and thereby, increase the decision quality and efficiency. Findings will also be used to build price-bidding policies for the freight-matching sites on the Internet, where the shippers post their loads to get price offers from the carriers. Finally, this work will contribute to the general theory of approximate dynamic programming and sensitivity analysis of stochastic control problems.

这笔赠款提供资金用于开发算法，评估车队管理模型的性能指标将如何变化，如果模型参数，如车队规模，负载可用性或终端容量，被修改。车队管理模型的主要目标是进行车辆-负载分配和车辆重新定位决策，从而优化性能指标，如利润、空驶里程或服务负载数量。然而，这些模型通常忽视的一个问题是，相关的性能指标将如何响应某些模型参数的变化而变化。例如，货运公司感兴趣的是，如果他们在系统中引入额外的车辆，或者如果他们提供额外的负载，他们的利润会增加多少。铁路公司希望估计满足随机托运人需求所需的最小轨道车数量。近似动态规划、无穷小扰动分析和组合优化的思想将被用来建立这些“敏感性分析”算法，这些算法随后将被用来做出战术决策，例如船队规模、定价和码头容量规划。这项研究将有助于他们更好地协调他们的战术和作战决策。目前，敏感性分析是通过耗时的方法进行的，这些方法涉及“物理”调整参数和重新运行模型。所开发的敏感性分析算法将快速地为决策者指出关键参数，从而提高决策质量和效率。调查结果还将用于为互联网上的货运匹配网站制定价格竞标政策，托运人在这些网站上发布货物以获得承运人的价格报价。最后，这项工作将有助于近似动态规划和随机控制问题的灵敏度分析的一般理论。