Multiple Stage Optimization of Stochastic Dynamic Transportation Networks

运输系统的决策过程是一个多阶段的问题，从长期的战略和战术规划决策到运营和实时/控制。 在实时、作战和战术阶段经历的供应和需求的不确定性以及潜在控制决策的有效性影响早期的战略决策。 然而，这些未来的影响往往被忽视的战略决策时，网络的优化。 通过忽视这些影响，规划者错过了机会，开发网络，更能抵抗波动，并能够充分利用潜在的信息，这项研究将开发的方法占运输系统的随机性（和动态），重点是代表行动，如实时路由和控制的追索权。 这将用于前一阶段的决策（如网络设计），其中将研究新的方法来解释潜在的追索权。 通过在整体战略、战术、作战和实时框架内查看系统，将探索阶段交互以及信息和不确定性在综合决策过程中的作用。 在这种观点下，信息和控制的真正价值既可以体现在实时改进方面，也可以体现在改进对先前决策的追索权方面。 除了研究问题，在这种情况下提出了几个基本的补充教育问题。 学生和实践运输工程师通常没有足够的技术背景，以充分了解随机和动态系统的复杂行为，以及如何利用实时数据的潜力，用于实际的在线操作目的。 本研究将明确解决这个问题，通过开发多阶段运输优化，教育模拟器和工具，将成功地传达与动态和随机运输系统的优化和控制相关的基本原理的新课程的教材给学生。 通过进一步发展这一领域的基础研究，并将其纳入教育课程，未来的规划者将能够更好地考虑到存在的不可避免的不确定性，以及在以后的阶段将提供的求助选择。 这反过来又有助于建立一个更强大、更有效的交通网络。

The decision making process for transport systems is a multistage problem, ranging from long-term strategic and tactical planning decisions to operational and real-time/control. Uncertainty in supply and demand experienced in the real-time, operational and tactical stages as well as the effectiveness of potential control decisions impact earlier strategic decisions. However, these future impacts are often neglected when strategic decisions are made for the optimization of a network. By neglecting such impacts, planners are missing opportunities to develop networks that are more resistant to fluctuations and capable of exploiting the full potential of information.This research will develop methods for accounting for the transport system stochasticity (and dynamics) with a focus on representing actions such as real-time routing and control as recourse. This will then be used within the context of prior stage decisions (such as network design), where new methods will be investigated to account for the potential recourse. By viewing the system within the overall strategic, tactical, operational, and real-time framework, stage interactions will be explored as well as the role of information and uncertainty within the integrated decision process. The true value of information and control under such a view can be given both in terms of real-time improvement and with the benefits from improved recourse on prior decisions. In addition to the research problems, several fundamental complementary educational issues are raised in this context. Students and practicing transportation engineers do not generally have sufficient technical background to fully appreciate the complex behavior of stochastic and dynamic systems, and the know-how to exploit the potential of real-time data for actual online operational purposes. This research will explicitly address this problem by developing teaching material for a new course on multi-stage transportation optimization, educational simulators, and tools that will successfully convey the fundamental principles related to the optimization and control of dynamic and stochastic transportation systems to students. By further developing the fundamental research of this area, and integrating it into the education curriculum, future planners will be able to better account for the unavoidable uncertainties that exist as well as the recourse options that will be available in later stages. In turn, this allows for the creation of a more robust and efficient transportation network.