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 will be 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.
