Course Details

Operational and System Analysis

Academic Year 2026/27

NPA017 course is part of 1 study plan

​ NPC-SIV Winter Semester 1st year

The course provides an overview of fundamental methods of operations research and systems analysis, with a focus on their application to problems in water management and civil engineering. It covers the formulation and analysis of decision-making and optimisation problems, linear and nonlinear programming, dynamic programming, multicriteria optimisation, graph theory and network analysis methods. The course also introduces the fundamentals of project management, risk analysis and selected computational intelligence methods, particularly artificial neural networks and genetic algorithms. Attention is paid to the practical application of individual methods and software tools in solving model problems.

Credits

6 credits

Language of instruction

Czech

Semester

winter

Course Guarantor

doc. Ing. Jaroslav Raclavský, Ph.D.

Institute

Institute of Municipal Water Management

Forms and criteria of assessment

course-unit credit and examination

Entry Knowledge

Knowledge of mathematics at the level of a bachelor's degree programme in Civil Engineering and basic user skills in Microsoft Excel.

Aims

Upon successful completion of the course, students will acquire the knowledge, skills and competences required to formulate, solve and evaluate basic optimisation and decision-making problems, with a focus on their application in water management and civil engineering.

Knowledge

Upon successful completion of the course, students will:

  • know the basic principles of operations research, optimisation and the systems approach to solving technical problems,
  • know the principles of linear, nonlinear and dynamic programming,
  • understand the fundamentals of multicriteria optimisation and decision-making,
  • know the fundamentals of graph theory and network analysis methods,
  • be familiar with the basic principles of project management and risk analysis,
  • know the basic principles of selected computational intelligence methods, particularly artificial neural networks and genetic algorithms,
  • understand the possibilities and limitations of applying individual methods to problems in water management and civil engineering.

Skills

Upon successful completion of the course, students will be able to:

  • formulate a basic decision-making or optimisation problem and identify its main variables, objectives and constraints,
  • select an appropriate basic approach for solving linear, nonlinear and multicriteria optimisation problems,
  • solve basic optimisation problems using appropriate software tools, particularly Solver in Microsoft Excel,
  • apply basic principles of graph theory and network analysis to model technical problems,
  • solve basic project management tasks using appropriate software tools,
  • interpret and evaluate the results of optimisation, decision-making and risk analyses,
  • explain the principles and potential applications of artificial neural networks and genetic algorithms in solving technical problems.

Competences

Upon successful completion of the course, students will be able to:

  • independently transform a basic technical problem into a form suitable for analytical or optimisation-based solution,
  • select and professionally justify an appropriate solution method with regard to the nature of the problem and the available data,
  • critically assess the results of a model-based solution, including its assumptions and limitations,
  • apply operations research and systems analysis methods to problems in water management and civil engineering,
  • professionally interpret, present and justify the proposed solution approach and the results obtained.

Basic Literature

JABLONSKÝ, J. Operační výzkum: kvantitativní modely pro ekonomické rozhodování. 3. vyd. Praha: Professional Publishing, 2007. ISBN 978-80-86946-44-3. (cs)
FIALA, P. Modely a metody rozhodování. 3., přeprac. vyd. Praha: Oeconomica, 2013. ISBN 978-80-245-1981-4. (cs)
NACHÁZEL, K.; STARÝ, M.; ZEZULÁK, J. a kol. Využití metod umělé inteligence ve vodním hospodářství. Praha: Academia, 2004. ISBN 80-200-0229-4. (cs)
TOMAN, M.; TOMAN, J.; MIKULECKÝ, P.; OLŠEVIČOVÁ, K.; PONCE, D. Inteligentní dispečerské rozhodovací systémy ve vodním hospodářství. Praha: České vysoké učení technické v Praze, 2009. ISBN 978-80-01-04452-0. (cs)

Recommended Reading

LOUCKS, D. P.; VAN BEEK, E. Water Resource Systems Planning and Management: An Introduction to Methods, Models, and Applications. Cham: Springer, 2017. ISBN 978-3-319-44232-7. (en)
BOZORG-HADDAD, O. (ed.). Essential Tools for Water Resources Analysis, Planning, and Management. Singapore: Springer, 2021. ISBN 978-981-33-4294-1. (en)

Prerequisites

Knowledge of mathematics at the level of a bachelor's degree programme in Civil Engineering and basic user skills in Microsoft Excel.

Offered to foreign students

Not to offer

Course on BUT site

https://www.vut.cz/en/students/courses/detail/303426

Lecture

13 weeks, 2 hours/week, elective

Syllabus

  • 1. Subject of operational and system analysis, basic terms and types of problems.
  • 2. Linear programming – Simplex method.
  • 3. Dual problem of linear programming, specific problems of linear programming.
  • 4. Transportation problem – solving by MODI method.
  • 5. Non-linear programming, method of objective function linearization.
  • 6. Non-linear programming – Lagrange method.
  • 7. Polyoptimal problems, pareto solving techniques.
  • 8. Combinatorial problems, bivalent programming.
  • 9. Graph theory, minimum graph frame and minimum graph trace.
  • 10. Network analysis – methods of project control.
  • 11. Dynamic programming.
  • 12. Neural networks, genetic algorithms.
  • 13. Risk analysis.

Exercise

13 weeks, 3 hours/week, compulsory

Syllabus

  • 1. Excel SOLVER.
  • 2. Linear programming – methods of graphical solution.
  • 3. Linear programming – Simplex method – Excel SOLVER.
  • 4. Dual problem of linear programming – Excel SOLVER.
  • 5. Distriubution problem – Excel SOLVER.
  • 6. Non-linear programming – Lagrange method.
  • 7. Non-linear programming – Lagrange method.
  • 8. Combinatorial methods – method Monte-Carlo.
  • 9. MS Project software tool.
  • 10. Graph theory – Critical Path Method.
  • 11. MS Project – project management.
  • 12. MS Project – project management.
  • 13. Credit.