This course will provide students to learn the methodologies of operations research and its applications to the real problem. The models include deterministic models (such as linear programming, multi-criteria decision analysis, data envelopment analysis, etc.) and stochastic models (such as Bayesian decision analysis, stochastic programming, Markov decision process, etc.). The course integrates the knowledge domains of the management and engineering, applied in capacity planning, facility layout, supply chain, manufacturing scheduling, performance evaluation, vendor selection and order allocation, Bin-packing, financial investment, etc. We develop the implementation capability of the information system in practice. Finally we should know how to solve the real problem systematically using optimization or statistical methods.

- Know the advanced techniques of operations research - Create theoretical model to solve the problem in real setting - System development and implementation

Prerequisites - Operations Research: ”Operations Research” in the IM department or equivalent. - Statistics: ”Statistics I” and “Statistics II” in the IM department or equivalent.

Birge, J. R., & Louveaux, F. (2011). Introduction to stochastic programming (2nd ed.). New York: Springer Verlag. Morse, P. M. and G. E. Kimball (1951, 2012). Methods of Operations Research. Dover Publications. Puterman, M. L. (2005). Markov Decision Processes: Discrete Stochastic Dynamic Programming. 2nd edition, Wiley-InterScience.