Optimization of resources and processes

This course is part of the programme
Master in Engineering and Management (Second Level)

Objectives and competences

The main goal of the course is to teach students the basics of optimization with an emphasis on resource and process optimization in production.

Students obtain the following competences:
• knowledge of theoretical concepts of optimization, elements of optimization problems, and types of optimization methods,
• the ability of recognizing characteristic problems of resource and process optimization in production, identifying their elements, and selecting an appropriate optimization method,
• competence of qualified partners to the designers and developers of computer-supported optimization procedures.


Knowledge of undergraduate-level mathematics and skills in using computers are required.


  1. Introduction
    • Course introduction
    • What is optimization?
    • Types of optimization problems
    • Gradient optimization methods
    • Operation research methods
    • Stochastic algorithms: local optimization, simulated annealing, evolutionary algorithms
    • Multiobjective optimization

  2. Optimization of production resources and processes
    • Problem examples
    • Sources of difficulties in problem solving
    • Ways of evaluating solutions in an optimization procedure
    • Requirements for computer optimization
    • Software tools

  3. Numerical optimization in practice
    • Identifying candidate solutions, considering constraints, selection of an optimization method
    • Process optimization based on numerical simulation
    • Statistical evaluation of results by stochastic methods
    • Case studies in process parameter optimization aimed at improving product quality

  4. Combinatorial optimization in practice
    • Constraints and search for feasible solutions
    • Scheduling as a typical problem example
    • Reactivity and robustness of scheduling systems
    • Case studies: task scheduling in energy intensive production operations, optimal workload assignment

Intended learning outcomes

The intended learning outcomes are as follows:
• understanding of concepts of optimization and optimization methods,
• successful identification of optimization problems in practice,
• the ability of formulating optimization problems,
• application of optimization methods and tools, assessment and interpretation of optimization results.


Selected chapters from the following books:

• M. Carter, C. C. Price, G. Rabadi: Operations Research: A Practical Introduction, 2nd edition. CRC Press, 2018. ISBN ISBN 9781498780100
• A. E. Eiben, J. E. Smith: Introduction to Evolutionary Computing, 2nd edition. Springer, 2015. ISBN 978-3-662-44873-1
• A. Kaveh: Advances in Metaheuristic Algorithms for Optimal Design of Structures. Springer, 2014. ISBN 978-3-319-05548-0
• F. Neumann, C. Witt: Bioinspired Computation in Combinatorial Optimization. Springer, 2010. ISBN 978-3-642-16543-6
• G. Rozenberg, T. Bäck, J. N. Kok (Eds.): Handbook of Natural Computing. Springer, 2012. ISBN 978-3-540-92909-3


• Seminar report, which assesses the ability of recognizing resource and process optimization problems, identifying their elements, and selecting an appropriate optimization method. • Written exam, which assesses the knowledge of theoretical concepts of optimization, optimization problem formulation, optimization methods and assessment of their results. 25 / 75

Lecturer's references

Prof. dr. Bogdan Filipič is a senior researcher and head of Computational Intelligence group in the Department of Intelligent Systems at the Jožef Stefan Institute, Ljubljana, Slovenia, and Adjunct Professor of Computer and Information Science, (rank full professor) at the University of Nova Gorica. He also gives courses at the Jožef Stefan International Postgraduate School, Ljubljana. His research interests are in evolutionary computation, stochastic optimization and intelligent computer systems. He is a principal investigator in several national and international projects in the fields of production process optimization, energy efficiency and IT support for cultural heritage preservation. He is also a founding member of the Slovenian Artificial Intelligence Society (SLAIS), and member of international associations IEEE and ACM.

Selected bibliography

TUŠAR, Tea, GANTAR, Klemen, KOBLAR, Valentin, ŽENKO, Bernard, FILIPIČ, Bogdan. A study of overfitting in optimization of a manufacturing quality control procedure. Applied Soft Computing, 2017, vol. 59, str. 77-87. [COBISS.SI-ID 30552359]

TUŠAR, Tea, FILIPIČ, Bogdan. Visualization of Pareto front approximations in evolutionary multiobjective optimization: A critical review and the prosection method. IEEE Transactions on Evolutionary Computation, 2015, vol. 19, no. 2, str. 225-245. [COBISS.SI-ID 27961383]

MLAKAR, Miha, PETELIN, Dejan, TUŠAR, Tea, FILIPIČ, Bogdan. GP-DEMO: Differential evolution for multiobjective optimization based on Gaussian process models. European Journal of Operational Research, 2015, vol. 243, no. 2, str. 347-361. [COBISS.SI-ID 27815207]

DOVGAN, Erik, JAVORSKI, Matija, TUŠAR, Tea, GAMS, Matjaž, FILIPIČ, Bogdan. Discovering driving strategies with a multiobjective optimization algorithm. Applied Soft Computing, 2014, vol. 16, no. 1, str. 50-62. [COBISS.SI-ID 27347495]

DOVGAN, Erik, JAVORSKI, Matija, TUŠAR, Tea, GAMS, Matjaž, FILIPIČ, Bogdan. Comparing a multiobjective optimization algorithm for discovering driving strategies with humans. Expert Systems with Applications, 2013, vol. 40, no. 7, str. 2687-2695. [COBISS.SI-ID 26503719]

FILIPIČ, Bogdan, VESANEN, Risto, LAITINEN, Erkki. Scalar vs. vector approach to bi-objective resource allocation in spatially distributed networks. International Journal of Innovative Computing and Applications, 2013, vol. 5, no. 3, str. 191-197. [COBISS.SI-ID 26954535]

DEPOLLI, Matjaž, TROBEC, Roman, FILIPIČ, Bogdan. Asynchronous master-slave parallelization of differential evolution for multiobjective optimization. Evolutionary Computation, 2013, vol. 21, no. 2, str. 261-291. [COBISS.SI-ID 25824807]

FISTER, Iztok, MERNIK, Marjan, FILIPIČ, Bogdan. Graph 3-coloring with a hybrid self-adaptive evolutionary algorithm. Computational Optimization and Applications, 2013, vol. 54, no. 3, str. 741-770. [COBISS.SI-ID 16111382]

KOROŠEC, Peter, ŠILC, Jurij, FILIPIČ, Bogdan. The differential ant-stigmergy algorithm. Inf. sci., 2012, vol. 192, no. 1, str. 82-97. [COBISS.SI-ID 23618855]

FISTER, Iztok, MERNIK, Marjan, FILIPIČ, Bogdan. A hybrid self-adaptive evolutionary algorithm for marker optimization in the clothing industry. Applied soft computing, 2010, vol. 10, no. 2, str. 409-422. [COBISS.SI-ID 22909479]