Graduate
Industrial AI Optimization Lab
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Advance Linear Programming
This course is an advanced OR I which we studied at undergraduate program. We reconsider linear programming in more detail by investigating linear algebra, convex analysis, and simplex method in matrix form and so on. Based on the theoretical understanding of LP, we try to develop the ability to solve large scale problem by using optimization software. For this purpose, we learn how to use Cplex, including setting up environment for Cplex, installing it and modeling a problem with it. Students need to complete one project by developing and implementing Cplex model to solve one decision making problem occurred in their own research areas.
 
Non-linear Programming
Most decision making issues occurred in the area of production and information systems are known as NP-hard, and it is very difficult to get an optimal solution. Therefore, it is very important to find out a good quality of solution efficiently and effectively. In this course, we deal with some basic search methods and meta-heuristics that can be used for solving either non-linear optimization problem or combinatorial optimization problem. Students need to complete one project by developing and implementing an algorithm to solve one decision making problem occurred in their own research areas, while pursuing to improve the ability of applying them to real situation.
 
Discrete System Analysis
This course deals with modeling and analysis methods for discrete event systems that can be useful for efficient design and analysis of production and service systems. Several basic methodologies and applications in discrete event system theory are introduced, which include Finite Automata, Petri nets, Stochastic Timed Automata, Markov Chain, Queuing Theory, and Dynamic Programming. Through this course, students can develop basic skills and ability for efficient design and control of industrial and information systems.