OPT: Optimization

Optimization group tackles challenges of difficult and large-scale optimization problems. The main areas of interest are:

Production Scheduling

Production planning and scheduling play an important role in supply chain management. Optimization group deals not only with classical scheduling and planning problems but also with the advanced and recent issues related to Smart Factories and Industry 4.0. A good example is a flexible production assuming alternative production plans where a single product can be produced in several different ways. Even though, this aspect can lead to significant savings in production, the existing MES and ERP systems cannot deal with it. Hence, we design complex optimization algorithms to address the problem.

Head of the group

Name: Přemysl Šůcha
Email: premysl.sucha(at)cvut.cz

Fundamental Research in Scheduling Algorithms

Many modern applications are of mixed criticality, where safety-critical tasks have to co-exist with less critical ones that are not subject to hard constraints. Recent research in real-time systems has yielded some promising techniques for meeting the two aspects – timing properties and efficiency. Mixed-criticality approach assumes multiple processing time values to be specified for each task depending on the levels of assurance. To enable this approach in practice, the design of novel scheduling algorithms is needed.

Human Resources Optimization

We have long-term experience with optimization of human resources. We are focused primarily on applications from health care and transport sector, but other domains interest us as well. Our specialty is problems with a high number of shift types allowing to cover personnel demand better. The algorithms designed at our group produces working plans for more than one hundred employees assuming planning horizons longer than one month and dozens of hard/soft constraints.

Energy Optimization

Energy efficiency of robotic cells is crucial for sustainable production; therefore, our research is also devoted to their optimization. Our holistic mathematical model of a robotic cell enabled us to develop efficient optimization algorithms that achieve about 25% of energy saving for an existing robotic cell at Skoda Auto.