Objectives and competences
The aims of this course are:
• to acquire and understand concepts and knowledge in the field of quantitative methods and models of logistics systems (LS),
• correctly identify problems in this field and gain knowledge for the construction of models and the use of quantitative methods in LS,
• understand the working mechanisms of methods and models of LS and be able to use them correctly to solve problems,
• acquire knowledge of the correct classification of various problems and use proper and appropriate procedures of quantitative methods and models of LS for a given problem,
• to gain an understanding of the theoretical backgrounds necessary for the correct interpretation of the obtained results of quantitative methods and models of LS and to assess their quality,
• to gain an understanding of the physical and mathematical mechanisms behind the problems and processes addressed within the logistics systems,
• learn to properly evaluate the adequacy and quality of quantitative methods and models of LS
• learn to correctly interpret the results of developed models and methods of LS and correctly draw conclusions based on designed models and applied methods.
Competences acquired by students:
• acquire theoretical knowledge in the field of quantitative methods and models of LS,
• have an in-depth understanding of quantitative methods and models of LS,
• understand the physical and mathematical mechanisms behind the quantitative methods and models of LS,
• solve complex problems in logistics systems using quantitative methods and models of LS,
• understand the working principles of quantitative methods and models of LS, useful both in this and other related subjects.
Content (Syllabus outline)
1. Basics of combinatorics and probability calculations
2. Graph theory: basic definitions, Euler and Hamilton graphs, trees, algorithms and methods for typical problems (Chinese post-man problem, traveling salesman problem, minimum spanning tree problem, maximum flow problem, shortest path problem, location problems).
3. Combinatorial optimization and use of optimization methods, procedures for solving problems from graph theory (exact methods, heuristics algorithms).
Learning and teaching methods
The subject includes various teaching and learning methods, such as: lectures in classical form, lectures via video presentations, films and webinars, student presentations and independent student studies.
Lectures: Students understand the theoretical frameworks of the course. Part of the lecture course is in a classroom while the rest is in the form of e-learning (e-lectures may be given via video-conferencing or with the help of specially designed e-material in a virtual electronic learning environment).
Tutorials: Students enhance their theoretical knowledge and are able to apply it. Part of the seminar is in a classroom while the rest is in the form of e-learning (e-Tutorials: may be given via video-conferencing or with the help of specially designed e-material in a virtual electronic learning environment).
Intended learning outcomes - knowledge and understanding
Knowledge and understanding:
The student will:
• be able to do master research methods, procedures, and processes in the field of quantitative methods and models of LS,
• be able for independent scientific research work in the field of quantitative methods and models of LS,
• understand the use of quantitative methods and models of LS with the ability of in-depth problem analysis and systems thinking in this area,
• develop the ability to integrate various concepts in the field of quantitative methods and models of LS, which lead to innovative solutions to the problems addressed,
• develop the ability to critically analyze complex knowledge, concepts, approaches, and strategies related to quantitative methods and models of logistics systems,
• be able to synthesize information in the field of quantitative methods and models of LS innovatively and recognize the value of knowledge or processes from the subject and practice perspective.
Study results will be checked (and measured) in different ways, as defined in shares (in%) in assessment methods.
Readings
Žerovnik, J. (2015). Principi modeliranja v logistiki: e-gradivo za predmet (Nova izd.). Fakulteta za logistiko. https://fl.um.si/digitalna-knjiznica/e-knjige/
Dragan, D. (2010). Principi modeliranja v logistiki: visokošolski učbenik. Fakulteta za logistiko Univerza v Mariboru. https://fl.um.si/digitalna-knjiznica/e-knjige/
Wilson, R. J., & Watkins, J. J. (1997). Uvod v teorijo grafov. Društvo matematikov, fizikov in astronomov Slovenije.
Winston, W. L. (2004). Operations research: applications and algorithms (4th, international student ed. izd.). Duxbury; Thomson; Brooks/Cole; Thomson Learning.
Hillier, F. S. (2008). Introduction to management science: a modeling and case studies approach with spreadsheets (3rd ed., str. XXII, 602). McGraw-Hill/Irwin.
Balakrishnan, V. K. (1997). Schaum’s outline of theory and problems of graph theory. McGraw-Hill.
Korte, B. (2006). Combinatorial Optimization: Theory and Algorithms (3rd ed.). Springer. http://link.springer.com/book/10.1007/3-540-29297-7
Additional information on implementation and assessment Presentation of a seminar work is a prerequisite for taking the exam
• Practical part of the exam 50%
• Theoretical part of the exam 20%
• Research paper and its presentation (e-lectures and e-exercises) 30%
