Content (Syllabus outline)
• Introduction: basic terms and examples in operational research of logistic, transport, and economic systems.
• Architecture and taxonomy of algorithms in operational research of logistic, transport, and economic systems.
• Algorithm design in operational research of logistic, transport, and economic systems.
• Complexity in operational research of logistic, transport, and economic systems.
• Complex mission planning and scenarios in uncertain environments.
• Constraints handling in operational research of logistic, transport, and economic systems: examples of deep sea navigation and economic dispatch with scenarios for performance evaluation.
• Robustness to mission failures and transport reliability.
• Energy autonomy and algorithms for economic planning in systems of systems.
• Performance comparison of algorithms for operational research of logistic, transport, and economic systems.
• Independent problem solving in proposed domains
Intended learning outcomes - knowledge and understanding
On completion of this course the student will be able to:
• demonstrate knowledge, understanding, application, synthesis, and evaluation of operational research of logistic, transport, and economic systems,
• analyze and design suitable systems in operational research of logistic, transport, and economic systems systems for solving the problems from the proposed domains,
• solve problems autonomously,
• create new knowledge from the proposed domains,
• apply and evaluate operational research of logistic, transport, and economic systems algorithms for control of systems.
Intended learning outcomes - transferable/key skills and other attributes
• Communication skills: oral lab work defence, manner of expression at written examination.
• Use of information technology: web based information search and use of software tools for operational research of logistic, transport, and economic systems.
• Problem solving: design and implementation of programs for operational research of logistic, transport, and economic systems.
Readings
• Zamuda, A. (2016, April). Differential evolution and large-scale optimization applications. IGI Global. https://www.igi-global.com/gateway/video/148878?lid=149972
• Evolutionary algorithms in engineering design optimization (p. IX, 302). (2022). MDPI. https://www.mdpi.com/books/pdfview/book/5118
• Zamuda, A., & Lloret, E. (2020). Optimizing data-driven models for summarization as parallel tasks. Journal of Computational Science, 42, 1–16. doi:10.1016/j.jocs.2020.101101
• Aleš Zamuda, José Daniel Hernández Sosa. Success history applied to expert system for underwater glider path planning using differential evolution. Expert Systems with Applications, 2019, vol. 119, pp. 155-170
• Carlos, L., Hernández Sosa, J. D., Greiner, D., Zamuda, A., & Caldeira, R. (2019). An approach to multi-objective path planning optimization for underwater gliders. Sensors, 19(24), 1–28. doi:10.3390/s19245506
• Glotić, A., & Zamuda, A. (2015). Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution. Applied Energy, 141, 42–56. doi:10.1016/j.apenergy.2014.12.020
• Zamuda, A., Crescimanna, V., Burguillo, J. C., Dias, J. M., Wegrzyn-Wolska, K., Rached, I., González-Vélez, H., Senkerik, R., Pop, C., Cioara, T., Salomie, I., & Bracciali, A. (2019). Forecasting cryptocurrency value by sentiment analysis: an HPC-oriented survey of the state-of-the-art in the cloud era. In High-performance modelling and simulation for big data applications: Selected Results of the COST Action IC1406 cHiPSet (pp. 325–349). Springer. https://link.springer.com/chapter/10.1007%2F978-3-030-16272-6_12
• Viktorin, A., Senkerik, R., Pluhacek, M., Kadavy, T., & Zamuda, A. (2019). Distance based parameter adaptation for Success-History based Differential Evolution. Swarm and Evolutionary Computation, 50, 1–17. doi:10.1016/j.swevo.2018.10.013
