Smart tools for transport and logistics
Syllabus
Forecasting in transport
• Simple regression analysis, Multiple regression analysis, Problems in regression analysis
• Time series methods
• Measures of forecasting accuracy
Optimization
• Basics of decision theory and optimization.
• Linear programming. Transport problem.
• Definition of graph and digraph. Optimization on graphs. The shortest path in the graph. Maximum throughput in the graph.
Information and communication tools
• Manipulation with Big Data in transport and logistics.
• Basics of data science.
• Basics of machine learning.
Goals and competencies
Goal/objectives: Connection of practice and theory, transfer of theoretical knowledge to the solution of concrete problems from practice.
Getting to know concrete data tasks in the selected business environment and the corresponding tools that can be used for these manipulations.
Competencies: The ability to connect theoretical and practical content, the ability to select an appropriate tool for solving a specific problem.
Basic literature
- Washington S. P., Karlaftis M. G., Mannering F. L. (2003): Statistical and econometric methods for transportation data analysis, Boca Raton: Chapman & Hall/CRC
- Rozga A., Grčić B. (2009): Poslovna statistika, Split: Ekonomski fakultet u Splitu
- Pallant J. (2011): SPSS Priručnik za preživljavanje, Beograd: Mikro knjiga