This unit aims at discrete optimization and its application for modelling and decision aid of real-life problems frequently encountered in practice. It consists of three parts: integer linear programming, combinatorial optimization techniques, and dynamic and stochastic decision making.
The first part addresses the modelling by mathematical programming, in particular by integer linear programming (IP), and relevant solution techniques.
The second part present generic methods to discrete optimization problems (exact methods, approximation and heuristics)
The third part addresses the models and methods for stochastic and dynamic decision making, i.e. for determination of optimal policies for control of a dynamic discrete event systems subject to random perturbations.
The more detailed content of each part is described below.
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