We are interested in solving large-scale Markov Decision Problems. The classical method of Dynamic Programming provides a mathematical framework for nding optimal solutions for a given Markov Decision Problem. However, for Dynamic Programming algorithms become computationally infeasible when the underlying Markov Decision Problem evolves over a large state space. In recent years, a new methodol...

the vehicle routing problem with backhauls (vrpb) as an extension of the classical vehicle routing prob-lem (vrp) attempts to define a set of routes which services both linehaul customers whom product are to be delivered and backhaul customers whom goods need to be collected. a primary objective for the problem usually is minimizing the total distribution cost. most real-life problems have othe...

The tree-depth of a graph G is the minimum height of any rooted forest F such that G ⊆ closure(F). We implemented a dynamic programming algorithm for finding the minimum tree-depth and designed heuristic algorithms by using betweenness, closeness, triangle, and degree centralities. We also proposed a heuristic algorithm based on the tree-depth decomposition tree for the graph coloring problem. ...

Radiotherapy treatment is often delivered in a fractionated manner over a period of time. Emerging delivery devices are able to determine the actual dose that has been delivered at each stage facilitating the use of adaptive treatment plans that compensate for errors in delivery. We formulate a model of the day-to-day planning problem as a stochastic program and exhibit the gains that can be ac...

We consider the problem of determining (for a short lifecycle) retail product initial and replenishment order quantities that minimize the cost of lost sales, back orders, and obsolete inventory. We model this problem as a two-stage stochastic dynamic program, propose a heuristic, establish conditions under which the heuristic finds an optimal solution, and report results of the application of ...