Advanced Decision Models




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Course Contents

Review of linear and integer linear programming. Multistage decision models: Dynamic programming. Network flow problems: Shortest path, maximum flow and minimum cost flow problems; Network optimization. Multi objective decision models: Analytic hierarchy and network processes. Nonlinear programming: Un constrained optimization; Lagrangian relaxation and KKT conditions; Convex optimization; Search, gradient and penalty based methods; Quadratic programming. Metaheauristics and their applications to combinatorial optimization problems such as scheduling and allocation problems. Stochastic decision models: Markov chains; Queues and queuing networks. 



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