Objectives and competences
The aims of this course are:
• to acquire and understand concepts and knowledge in the field of mathematical models and methods within the scope of management of logistics systems (MLS).
• correctly identify problems in this area and gain knowledge for the construction of models and the use of methods within the scope of MLS.
• understand the working mechanisms of methods and models within the scope of MLS, and be able to use them correctly to solve problems.
• to acquire knowledge of the correct classification of various problems and the ability to use the correct and appropriate methods and models within the scope of MLS for a given problem.
• to gain an understanding of the theoretical backgrounds necessary for the correct interpretation of the obtained results of methods and models within the scope of MLS and assessment of their quality.
• to gain an understanding of the physical and mathematical mechanisms behind the problems and processes discussed within the scope of MLS.
• learn to properly evaluate the adequacy and quality of the conducted methods and models within the scope of MLS, and to be able to correctly use the appropriate metrics to test their validity.
• learn to correctly interpret the results of the used methods and models within the scope of MLS and to correctly draw conclusions based on these methods and models.
Competences acquired by students:
• acquire theoretical knowledge in the field of mathematical models and methods within the scope of MLS;
• have an in-depth understanding of mathematical models and methods within the scope of MLS;
• get to know and understand metrics in the field of mathematical models and methods within the scope of MLS;
• understand the physical and mathematical mechanisms behind mathematical models and methods within the scope of MLS;
• solve complex problems in logistics systems using mathematical models and methods within the scope of MLS.
• understand the working principles of mathematical models and methods within the scope of MLS, useful both within this and other related subjects.
Content (Syllabus outline)
THEORY OF SYSTEM CONTROL: Ontology of system control, system models, deterministic and stochastic systems, control as decision making, optimal model-based control. Orders’ Controllers and supply chain synchronization for the bullwhip effect compensation.
MODELING AND SIMULATION OF LOGISTIC SYSTEMS: Theory of discrete systems modelling, simulation tools, introduction to Scilab, simulation of discrete systems, simulation of stochastic logistic processes, Monte carlo simulation.
THE USE OF STATISTICAL MODELS AND METHODS FOR THE SUPPLY CHAIN MANAGEMENT, TRANSPORTATION AND TRAFFIC:
The use of multivariate statistical analysis, structural equation and other statistical models in logistics, supply chain management, transportation and traffic. The main emphasis is on increasing a companies’ efficiency, as well as improving transportation and traffic safety.
METHODS FOR CONTROL OF LOGISTICS SYSTEMS: Optimization of transportation and logistics systems, job scheduling in logistic systems, heuristic procedures and meta-heuristics for the optimization of logistic systems, theory of time-series forecasting, modeling and control of distribution networks.
EXAMPLES OF LOGISTICS SYSTEMS CONTROL:
Alocation of distribution centers, scheduling examples in logistic systems, inventory control, demand forecasting, modeling of transportation in distribution networks, optimization of the problems in the queueing theory.
Learning and teaching methods
Lectures: Student is introduced to the theoretical part of the subject. Student is pointed to consolidate the theoretical part of the subject. Additionally, student is pointed to gain the understanding for the solving of more demanding application problems
Intended learning outcomes - knowledge and understanding
Knowledge and understanding:
The student will be able to:
• Master research methods, procedures, and processes in the field of quantitative methods and models within the scope of MLS.
• Able for independent scientific research work in the field of quantitative methods and models within the scope of MLS.
• understand the use of quantitative methods and models within the scope of MLS with the ability of in-depth problem analysis and systems thinking in this area.
• Able to cooperate creatively in solving problems in logistics environments.
• acquire general and specific knowledge in the field of quantitative methods and models within the scope of MLS.
• Develop the ability to integrate various concepts in the field of quantitative methods and models within the scope of MLS, 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.
• Able to synthesize information in the field of quantitative methods and models within the scope of MLS innovatively and recognize the value of knowledge or processes from the subject and practice perspective.
Intended learning outcomes - transferable/key skills and other attributes
Transferable/Key Skills and other attributes:
Capability of independent scientific and research work in the field of logistics systems control. Capability of efficient solving of more complicated problems in this field. Capability of dealing with research methods employment in this field, with the possibility of deep analysis and system reflection of the identified problems. Capability of creative collaboration in order to deal with solving of more difficult problems in logistics environments.
Readings
1 Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (2008). Time series analysis: forecasting and control (4th ed.). Wiley.
2 Dragan, D. (2009). Upravljanje logističnih sistemov: visokošolski učbenik. Fakulteta za logistiko.
3 Dragan, D. (2014). Statistika in uvod v regresijske modele v Matlabu pri optimizaciji logističnih procesov: učbenik (1. izd.). Fakulteta za logistiko. https://fl.um.si/knjiznica/digitalna-knjiznica/e-knjige/
4 Dragan, D. (2013). Stohastični procesi v logistiki: visokošolski učbenik. Univerza v Mariboru, Fakulteta za logistiko. https://fl.um.si/knjiznica/digitalna-knjiznica/e-knjige/
5 Dragan, D. (2010). Optimizacija logističnih procesov: visokošolski učbenik. Fakulteta za logistiko Univerza v Mariboru. https://fl.um.si/knjiznica/digitalna-knjiznica/e-knjige/
6 Dragan, D. (2010). Principi modeliranja v logistiki: visokošolski učbenik. Fakulteta za logistiko Univerza v Mariboru. https://fl.um.si/knjiznica/digitalna-knjiznica/e-knjige/
Additional information on implementation and assessment Report about conducted research work of the chosen learning unit 70%
Public presentation of report 30%
