Stefan Wagner,
University of Applied Sciences of Upper Austria,
Hagenberg, Austria

"Algorithm and Experiment Design with HeuristicLab. An Open Source Optimization Environment for Research and Education"

This tutorial demonstrates how to apply and analyze metaheuristic optimization algorithms using the HeuristicLab open source optimization environment. It is shown how to parameterize and execute evolutionary algorithms to solve various optimization problems (e.g., traveling salesman, vehicle routing, quadratic assignment, symbolic regression). The participants learn how to assemble different algorithms and parameter settings to large-scale optimization experiments and how to execute such experiments on multi-core or cluster systems. Furthermore, the experiment results are compared using HeuristicLab’s interactive charts for visual and statistical analysis to gain knowledge from the executed test runs. To complete the tutorial, it is sketched briefly how HeuristicLab can be extended with further optimization problems and how custom optimization algorithms can be modeled using the graphical algorithm designer.


Ivan Ryzhikov,
Joint Stock Company "The Gulidov Krasnoyarsk Non-Ferrous Metals Plant"
Krasnoyarsk, Russia

"Mathematical Modeling and Optimization in Digital Transformation"

Digital transformation is a well-known trend, which consists of different but somehow related parts, and here we consider its fundamental components: data science, business intelligence and business analysis, which are the fields of high importance and high demand. These areas are widely spread and the IT-product market offers a variety of different so-called “out of the box solutions” to satisfy the organization needs in solving some of the problems. However, there are still many problems, which require research and development of specific approaches, and therefore, software tools. Moreover, when talking about problems, which could be solved with the methods of each particular discipline, one may face an ambiguity of definitions and, actually, an intersection of these fields. Why is it so? What do these disciplines have in common? What are those scientific fields, methods and tools, which would be useful for system analysts in the organizations, whatever component they are involved in, be it data science, business analysis or something similar? In the current tutorial, we take a brief look at these questions in the case of precious metals manufacturing and a little bit more, describe some particular problems and approaches for solving them. In addition, we discuss the dynamical system modeling, the workflow modeling, decision-making support system development and particular optimization problems.