|Institution:||Texas State University – San Marcos|
|Keywords:||Scheduling; Taems; Distributed Constraint Optimization|
|Full text PDF:||https://digital.library.txstate.edu/handle/10877/6074|
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. Advisors/Committee Members: Podorozhny, Rodion (advisor), Yang, Guowei (committee member), Guirguis, Mina S. (committee member).