CAREER: Dynamic Programming Approximations for Large-Scale Logistics Problems

该补助金为研究提供资金，以开发用于解决大规模动态和随机优化问题的近似方法。 这些类型的问题在许多应用中都会遇到，特别是当决策必须定期做出时，决策者并不确切知道未来将如何影响目标和正在考虑的过程的演变。 将采用的具体方法是发展动态规划近似法。 这些方法还将用于解决物流和运输中遇到的一些重要问题。 该补助金还为与研究有关的教材的开发提供资金。 模拟驱动的物流游戏将有助于教玩家如何在动态和随机环境中做出决策。 该研究将有助于我们解决复杂的动态和随机优化问题的能力。这将提高决策者在动态变化的环境中做出正确决策的能力，因为未来的结果不确定。 将制定实施这些方法的指导方针，以使从业人员更容易获得该技术。通过与物流和运输业的合作，他们将得到帮助，以改善他们的一些业务。 将开发的教材可用于研究生和本科教育，以及工业决策者的培训。

This grant provides funding for research to develop approximation methods for the solution of large-scale dynamic and stochastic optimization problems. These types of problems are encountered in many applications, especially when decisions have to be made on a regular basis, and the decision maker does not know exactly how the future will impact the objective and the evolution of the process under consideration. The specific approach that will be used is the development of dynamic programming approximations. The methods will also be used to address a number of important problems encountered in logistics and transportation. The grant also provides funding for the development of teaching materials associated with the research. Simulation driven logistics games will be developed that will help to teach the player how to make decisions in dynamic and stochastic environments. The research will contribute to our capability to solve complex dynamic and stochastic optimization problems. This will improve the ability of decision makers to make good decisions in a dynamically changing environment with uncertainty about future outcomes. Guidelines for the implementation of these methods willbe developed to make the technology more accessible to practitioners. Through collaboration with the logistics and transportation industries, they will be helped to improve some of their operations. The teaching materials that will be developed can be used for graduate and undergraduate education, as well as the training of decision makers in industry.