Course detail

Operation research in water management

FAST-DPB026Acad. year: 2026/2027

The course focuses on advanced applications of operations research and systems analysis methods to decision-making, optimisation and research problems in water management. It covers linear and nonlinear programming, combinatorial methods, multicriteria optimisation, graph theory and network analysis methods. Attention is also paid to project management, system failure and reliability analysis, reliability modelling, and risk analysis and management.

The course also introduces selected computational intelligence methods, particularly artificial neural networks and genetic algorithms. Emphasis is placed on the selection of appropriate methods, model formulation and solution, interpretation of results, and critical assessment of the possibilities and limitations of the applied approaches. Selected methods are applied to case studies and professional or research problems in water management.

Language of instruction

Czech

Number of ECTS credits

8

Assignment to study programme types

Doctoral

Mode of study

Not applicable.

Department

Institute of Municipal Water Management (VHO)

Entry knowledge

Knowledge of fundamental operations research and systems analysis methods at the level of a master's degree programme, particularly the basics of mathematical optimisation, graph theory and the use of appropriate computational tools.

Rules for evaluation and completion of the course

The course is delivered through consultations and independent study of professional and scientific sources.

A prerequisite for taking the oral examination is the completion and submission of a semester project focused on the application of a selected operations research or systems analysis method to a specific professional or research problem in the field of water management.

The semester project must demonstrate correct formulation of the problem, appropriate selection and application of the method, correct interpretation of the results, and the ability to critically assess the possibilities and limitations of the applied approach. If deficiencies are identified, revision or correction of the project may be required.

The oral examination focuses on the subject matter of the course, the methodology applied and the results of the semester project, and on assessing the ability to professionally discuss the selected approach and possible alternative solutions.

The course is assessed on a pass / fail basis. A pass is awarded if the semester project is assessed as satisfactory and the oral examination is successfully completed. If either of these conditions is not fulfilled, the final assessment is fail.

Aims

Knowledge

Upon successful completion of the course, students will:

  • understand advanced principles of operations research and systems analysis and their applications in water management,
  • know the principles of linear and nonlinear optimisation, combinatorial methods and multicriteria optimisation,
  • understand the principles of graph theory and network analysis,
  • be familiar with methods for reliability, failure and risk analysis,
  • know the principles of selected computational intelligence methods, particularly artificial neural networks and genetic algorithms,
  • understand the possibilities and limitations of individual methods when addressing professional and research problems in water management.

Skills

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

  • formulate a decision-making, optimisation or research problem and transform it into an appropriate model,
  • select and apply an appropriate method with regard to the nature of the problem and the available data,
  • develop and solve models using appropriate computational and software tools,
  • analyse and interpret results and assess their sensitivity, reliability and limitations,
  • apply selected operations research methods to specific problems in water management,
  • prepare and professionally defend a case study or the solution of a research problem.

Competences

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

  • independently solve complex decision-making and optimisation problems in water management,
  • critically assess the suitability of selected methods, modelling assumptions and obtained results,
  • combine different analytical, optimisation and modelling approaches according to the nature of the problem,
  • apply operations research and systems analysis methods in their own professional and research activities,
  • professionally interpret, discuss and defend the applied methodology and the results obtained.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

FIALA, P. Modely a metody rozhodování. 3., přepracované vydání. 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)
VOTRUBA, L.; HEŘMAN, J. a kol. Spolehlivost vodohospodářských děl. Praha: Česká matice technická, 1993. ISBN 80-209-0251-1. (CS)

Recommended reading

BOZORG-HADDAD, O., ed. Essential Tools for Water Resources Analysis, Planning, and Management. Singapore: Springer, 2021. ISBN 978-981-33-4294-1. (EN)
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)

Classification of course in study plans

  • Programme DPA-V Doctoral 1 year of study, summer semester, compulsory-optional
  • Programme DKA-V Doctoral 1 year of study, summer semester, compulsory-optional
  • Programme DPC-V Doctoral 1 year of study, summer semester, compulsory-optional
  • Programme DPC-V Doctoral 1 year of study, summer semester, compulsory-optional

Type of course unit

 

Lecture

39 hours, optionally

Teacher / Lecturer

Syllabus

  • 1. - 4. Selected problems of non-linear programming and methods of solving them.
  • 5. - 8. Multi-criteria optimization tasks, methods of solution.
  • 9. - 12. Use of artificial neural networks, genetic algorithms, available software for their use.13. Risk analysis