Approximate dynamic programming in transportation and logistics: a unified framework
Approximate dynamic programming in transportation and logistics: a unified framework
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运输和物流中的近似动态规划：统一框架
DOI：
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发表时间：
2012
影响因子：
2.4
通讯作者：
Belgacem Bouzaïene
中科院分区：
文献类型：
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作者：
Warrren B Powell;H. Simão;Belgacem Bouzaïene
确定性优化在运输和物流领域有着丰富的应用，它代表了一个成熟的领域，具有既定的建模和算法策略。相比之下，序列随机优化模型（动态程序）一直受到缺乏通用建模框架的困扰，并且算法策略似乎无法扩展到现实世界的交通问题。本文旨在作为近似动态规划的建模和算法框架的教程;然而，我们对近似动态规划的观点是相对较新的，这种方法对交通研究界来说是新的。我们提出了一个简单而精确的建模框架，使人们有可能将大多数算法策略整合到四个基本类的政策，其设计代表这些动态程序的近似解决方案。然后，本文使用的问题，在运输和物流，以表明设置中的四个类的政策代表一个自然的解决方案的战略，突出了一个事实，即设计有效的政策，这些复杂的问题将仍然是一个令人兴奋的研究领域多年。沿着的方式，我们提供了一个动态规划，随机规划和随机搜索之间的联系。
Deterministic optimization has enjoyed a rich place in transportation and logistics, where it represents a mature field with established modeling and algorithmic strategies. By contrast, sequential stochastic optimization models (dynamic programs) have been plagued by the lack of a common modeling framework, and by algorithmic strategies that just do not seem to scale to real-world problems in transportation. This paper is designed as a tutorial of the modeling and algorithmic framework of approximate dynamic programming; however, our perspective on approximate dynamic programming is relatively new, and the approach is new to the transportation research community. We present a simple yet precise modeling framework that makes it possible to integrate most algorithmic strategies into four fundamental classes of policies, the design of which represents approximate solutions to these dynamic programs. The paper then uses problems in transportation and logistics to indicate settings in which each of the four classes of policies represents a natural solution strategy, highlighting the fact that the design of effective policies for these complex problems will remain an exciting area of research for many years. Along the way, we provide a link between dynamic programming, stochastic programming and stochastic search.