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Collaborative Research: Emerging Optimization Methods for Planning and Operating Shared Mobility Systems under Uncertain Budget and Market Demand

协作研究：预算和市场需求不确定下规划和运营共享出行系统的新兴优化方法

基本信息

批准号：
1727478
负责人：
Mengshi Lu
金额：
$ 12.5万
依托单位：
Purdue University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-01-01 至 2020-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1727478&HistoricalAwards=false
关键词：
Collaborative Research Emerging Optimization Methods

项目摘要

Advances in networked communication systems have enabled the use of shared mobility forms including carsharing and ridesharing. Both government and private sectors engaged in planning shared mobility systems must choose between introducing new, shared mobility programs versus expanding existing ones, under uncertain budget and market demands. In this project, the PI will address the issue of demand uncertainty in design of shared mobility strategies considering shared fleet size, type, location design, and operational activities such as real-time vehicle routing, redistribution, and charging for carsharing systems; in addition to service region planning and shared mobility. The success of this project will: (i) advance both the theoretical and the computational frontiers of optimization methods for use in solving new transportation problems; and (ii) impact applications of shared mobility that relate to critical civil infrastructures, supply chain & logistics, and other service industries. Education plans, including promoting female and underrepresented minority groups in science, engineering, and management, will be collaboratively undertaken through the PIs' involvements in various education initiatives at the University of Michigan and Purdue University.The objective of this collaborative research is to derive high-fidelity, data-driven mathematical models and provably efficient numerical algorithms that innovatively combine optimization and reinforcement learning for shared mobility system design and operations. In specific, we characterize shared mobility demand response as a multi-stage information revealing process, and abstract the corresponding decision process as sequential resource planning, allocation, and task prioritization, adjusted by varying decisions in later stages. We derive models and solution methods based on single-, two- and multi-stage stochastic optimization and dependent on full knowledge of demand distributions. We also investigate data-driven distributionally robust optimization methods and machine learning approaches to address ambiguous demand distributions and budget uncertainty. The derivation, validation, and calibration of this study aim at (i) formulating appropriate optimization-based models to characterize decision-data interdependence in complex shared mobility systems; (ii) deploying data-driven, distribution-free approaches for handling the distribution ambiguity of multi-sourced uncertainties in emerging shared mobility services; (iii) integrating learning approaches to dynamically adapt to endogenous system information and enhancing solutions from multi-stage optimization processes; (iv) designing efficient computational methods with solution quality guarantees to enable practical use of the models.

联网通信系统的进步使共享出行形式的使用成为可能，包括拼车和拼车。在预算和市场需求不确定的情况下，参与规划共享移动系统的政府和私营部门必须在引入新的共享移动计划和扩大现有计划之间做出选择。在这个项目中，PI将解决共享机动性战略设计中的需求不确定性问题，考虑到共享车队的规模、类型、位置设计和运营活动，如实时车辆路线、再分配和对汽车共享系统的充电；此外，服务区规划和共享机动性。该项目的成功将：(I)推动用于解决新的交通问题的优化方法的理论和计算前沿；(Ii)影响与关键民用基础设施、供应链和物流以及其他服务行业相关的共享移动性的应用。教育计划，包括在科学、工程和管理领域促进女性和代表性不足的少数群体，将通过私人投资机构参与密歇根大学和普渡大学的各种教育倡议而合作进行。这种合作研究的目标是得出高保真、数据驱动的数学模型和被证明有效的数值算法，创新地将优化和强化学习结合在一起，用于共享移动系统设计和操作。具体地说，我们将共享移动性需求响应描述为一个多阶段的信息揭示过程，并将相应的决策过程抽象为顺序的资源规划、分配和任务优先级排序，并根据后续阶段的不同决策进行调整。在充分了解需求分布的基础上，建立了基于单阶段、两阶段和多阶段随机优化的模型和求解方法。我们还研究了数据驱动的分布式稳健优化方法和机器学习方法，以解决模糊的需求分布和预算不确定性。这项研究的推导、验证和校准的目的是：(I)建立适当的基于优化的模型来表征复杂共享移动系统中决策数据的相互依赖；(Ii)采用数据驱动的、无分布的方法来处理新兴共享移动服务中多源不确定性的分布模糊性；(Iii)整合学习方法以动态适应内生系统信息并增强来自多阶段优化过程的解决方案；(Iv)设计具有解质量保证的高效计算方法，以使模型能够实际使用。