Evaluation and Adaptation of a Constraint Optimization and Distributed, Anytime A* Algorithm to Design-To-Criteria Scheduling Problem
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Scheduling complex problem solving tasks where tasks are interrelated and there are multiple different ways to go about achieving a particular task is a computationally challenging problem. In this thesis, we study current approaches to solving such complex scheduling problems, and propose two new optimization techniques, which exploit A* based optimization, and constraint based optimization. We then perform an analytical comparison and computational complexity estimate for the efficiency enhancement achieved by these approaches, as compared against a base line case of “god’s view” based optimal policy evaluation for same problems.