Industrial Systems Modeling and Optimization


This course provides students with a broad range of advanced methods and techniques, enabling them to model and solve the various complex large-scale optimization problems, arising in design, operations and control of real-life industrial engineering systems. After completing the course “Operations Research Models and Methods” and this course “Industrial Systems Modeling and Optimization”, students should be able to analyse real-life industrial engineering problems, build effective optimization models and select or adapt the most suitable optimization methods for their solution. They should be able to interpret and evaluate the quality of the resulting solution and its limits. As a supporting theme, the course will also emphasize effective modeling techniques, the use of modeling languages and the use of major solvers.


Optimization of Large-Scale Linear Systems
•  Revised and dual simplex algorithms
•  The primal-dual simplex algorithm
•  Primal and primal-dual interior-point algorithms
•  The Dantzig-Wolfe decomposition method
•  The Column Generation algorithm
•  Applications in network optimization
Optimization of Large-Scale Discrete Systems
•  The Branch-and-Cut algorithm
•  Lagrangian relaxation and duality
•  Heuristics and approximation algorithms
•  The Benders decomposition method
•  The Branch-and-Price algorithm
•  Applications in networks design and operations
Optimization of Stochastic Systems
•  Uncertainty and Modelling Issues
•  Two- and multistage stochastic programs with recourse
•  Probabilistic and stochastic integer programs
•  Value of Information and the Stochastic Solution
•  L-Shaped method and other algorithmic techniques
Decision-Making in Stochastic Dynamic Systems
•  Markov decision processes
•  Optimality equations, policies and value functions
•  Linear programming models for stochastic dynamic systems
•  Value, policy, and hybrid value-policy iteration methods
•  Successive approximations and direct policy search