Numerous problems found in industries and scientific research can be formulated as optimization problems and tackled with various optimization approaches. As a matter of fact, almost every product, process or system has the potential to be “improved” so that the applications of optimization are countless.

In industrial problems, optimization is very often about the maximization of profit, productivity, efficiency and quality as well as minimization of time, cost, waste and risk. While the task of an optimization algorithm is to search promising solutions in an efficient way, a practical problem almost always requires a solution, or a decision, to be made for solving the underlying problem. In this case, the decision-making process, mostly with the involvement of a single or a group of human decision makers, is an equally important task as the optimization process. In other words, industrial optimization goes naturally hand-in-hand with decision-making support and decision analysis.

There are many exciting new developments, both in research and industrial practice when it comes to the interlacing of optimization techniques and decision analysis methods. To name a few examples: (1) optimization in a truly multi-objective context; (2) interactive multi-objective optimization to incorporate the preferences of the decision maker; (3) new software to support the interactive visualization of optimization data for decision analysis. While the course will progressively cover these advanced topics, it will commence with the general theories and methodologies in classical optimization models and modern metaheuristic algorithms.

In summary, this course will prepare graduate students to have the sufficient knowledge to follow the research frontier in the field of Optimization and Decision Analysis as well as some essential skills to solve real-world industrial-scale problems.