prof. Ing. Petr Stehlík, CSc., dr. h. c.

Institute of Process and Environmental Engineering

Students are expected to have prior knowledge in statistical data analysis, data visualization, mass balance calculations, and Python programming. Basic understanding of technical documentation and teamwork is also required. 

The course is completed with a graded credit. Requirements:

• active participation in class (maximum 2 absences allowed),

• continuous progress on the project (control milestones),

• submission and defence of the final report and presentation.

Assessment is based on the weighted average:

• Project report – 20%

• Technical solution and code – 50%

• Oral defence – 30%

Grading A–F follows the scale defined by the course supervisor.

Czech

The course aims to develop students’ project, analytical, and presentation skills in solving a complex engineering task.

• It teaches students to work in an environment simulating an engineering project and client communication.

• Students will practice applying statistics, data analysis, and optimization methods to practical problems.

• The course strengthens competencies in mathematical statistics and operations research (including network flow modelling and scenario-based optimization).

• It supports teamwork, argumentation, critical thinking, and presentation skills.

• Students are introduced to project management, work-package planning, and workload estimation.

• The course guides students toward independent decision-making, documentation, and defence of results.

Programme N-PRI-P: Process Engineering, Master's
branch ---: no specialisation, 3 credits, compulsory

78 hours, compulsory

Lectures and practical blocks – 78 hours (13 × 6 hours per week); a combination of theory, workshops, and team-based project work. 

Week 1 – Introduction and Project Definition
Students are introduced to course organization, sample project topics, and basics of data work and Python. Teams select a project, conduct initial brainstorming, and prepare a draft problem definition and project proposal.

Week 2 – Project Proposal and Data Preparation
Teams build their project proposal and validate it with the client. They also learn the basics of data cleaning and dataset preparation.

Week 3 – Cluster Analysis and Stratification
Students learn the principles of clustering and its use in data segmentation and apply the methods to their project dataset.

Week 4 – Time Series and Forecasting
Teams learn to analyse time series, detect trends and anomalies, and build forecasts using their own project data.

Week 5 – Location and Allocation Problems
Students learn optimization fundamentals and experiment with transport problems in Excel and Python.

Week 6 – Multicriteria and Scenario Optimization
Students explore scenario design, risk treatment, and multicriteria optimization and build scenario-based models.

Week 7 – Review and Advanced Modelling
Teams refine their models while studying network flows, capacity constraints, and additional modelling techniques.

Week 8 – Routing and Scheduling
Students learn principles of route planning and scheduling, including TSP/VRP solved with OR-Tools.

Week 9 – First Project Presentation
Teams present their first version of the solution and receive detailed feedback.

Week 10 – Advanced Topic I
An advanced topic tailored to the teams’ specific project needs is covered.

Week 11 – Second Project Presentation
Teams present advanced versions of their solution to the client and receive targeted recommendations.

Week 12 – Advanced Topic II
Final refinements of models, analyses, and interpretations.

Week 13 – Final Project Defence
Final presentation before the committee and submission of complete documentation.