Undergraduate
Industrial AI Optimization Lab
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Operations Research & Practice I
These days it is necessary to efficiently resolve various decision making problems occurred in the areas of production, transportation, and telecommunication where economic situation is changed drastically. Specifically, in this course, we deal with linear programming(LP) method which represents those problems by using linear objective function and linear constraints. We learn basic idea and solution procedure of simplex method for LP and apply it to some examples from application areas such as transportation problem, assignment problem, and several network flow problems. Through this course, we expect that students can develop their abilities to understand, analyze, and solve the problems, while suggesting some alternatives for improving the current situation.
 
Operations Research & Practice II
Based on Operations Research & Practice I, in this course we consider several other kinds of solution and analysis methodologies. They can be classified into two groups such as deterministic and stochastic methods. In case of deterministic ones, we consider integer programming(IP), dynamic programming(DP), non-linear programming(NLP) and meta-heuristics. For stochastic methods, we deal with Markov chain and Queueing theory. These methods have their own properties, which can be used to compromise each other in order to resolve decision making problems. Therefore, after this course, students can have various perspectives for modeling and analyzing a system, depending on their objectives and situation.