Objectives and competences
Objectives:
• to define the role of modern technical solutions and their application in intralogistics,
• to present possible technical-technological solutions for the application in smart factories and smart warehouses,
• to present layout planning and analysis of flexible production and storage systems,
• to explain the importance of automated and robotised solutions in smart factories and smart warehouses,
• to define a systematic approach to solving various technical challenges in intralogistics,
• to present an example of modeling and optimization of intralogistics systems using advanced models and computer-aided tools,
• to upgrade knowledge in the field of planning and development of new models for solving the challenges of intralogistics systems.
Competences that students acquire:
• get familiar and to understand the operation of various modern technical solutions in smart factories and smart warehouses,
• gain the ability to perform and to analyse the layout of flexible production and storage systems.
• gain the ability to select and to implement automated and robitised transport and storage technical-technological solutions
• gain the ability to evaluate and select the optimal transport device for storing, order-picking, workplace supply, packaging and loading/unloading system.
• to be able to create a model of a factory or warehouse and to simulate, optimize and analyze transport, storage and supply processes in intralogistics.
Content (Syllabus outline)
1. Intralogistics (layout, automated and robotised storage, internal transport, workplace supply, and loading/unloading system, methodologies and analytical/numerical models, throughput performance and traceability).
2. Smart factories (smart products, machines and process, new business models "Batch size 1", concept of the working environment "Operator 4.0", smart factory flexible platform model).
3. Smart warehouses (process in a smart warehouse, robotisation of warehouse process, augment and virtual reality, smart pallets and bins, artificial intelligence, AGV and autonomous mobile robots AMR, localization and navigation).
4. Systematic layout planning of flexible production and storage systems (material flow diagrams, rough and fine layout planning, Sankey diagrams, material flow matrixes, DIJKSTRA algorithm, analytical and numerical layout planning of flexible production and storage systems).
5. Automated storage systems and mobile robots for supporting order-picking system (autonomous mobile robots AMR, collaborative mobile robots KMR, autonomous forklift trucks AGV, analytical and numerical model to determine the system performance).
6. Simulation modelling of transport and storage systems in intralogistics (discrete and continuous optimization of transport and storage systems in intralogistics, single- and multi-objective optimization, digital twin of transport and storage system).
Learning and teaching methods
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 laboratory 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
At the end of the course, the student is able to:
• understand and critically evaluate the possibility of using modern technical solutions to improve processes in intralogistics,
• plan, analyse and evaluate layout of flexible production and storage systems,
• plan, analyse and optimize solutions of automated transport and storage systems in intralogistics,
• plan and analyse mobile robot systems to support the process of order-picking,
• use standards (ISO, EN) and technical guidelines (VDI, FEM) for modelling intralogistics systems,
• apply analytical models and computer supported tools for modelling intralogistics systems,
• assess the potential of mobile autonomous vehicles in other areas of opportunity (use in hospitals, city centers, airports).
Readings
• Lerher, T. (2022). Avtomatska vozila in mobilni roboti v intralogistiki (1. izd.). Univerzitetna založba. https://doi.org/10.18690/um.fs.3.2022
• Fottner, J., Galka, S., Habenicht, S., Klenk, E., Meinhardt, I., & Schmidt, T. (2022). Planung von innerbetrieblichen Transportsystemen: Fahrzeugsysteme (str. XI, 231). Springer Vieweg.
• Martin, H. (2021). Technische Transport- und Lagerlogistik. Springer Vieweg.
• Ten Hompel, M., Bauernhansl, T. & Vogel-Heuser, B. (Eds.). (2020). Handbuch Industrie 4.0. Bd. 3, Logistik (3. Aufl.). Springer Vieweg.
• Bartholdi, J. J. & Hackman, S. T. (2019). Warehouse and distribution science, Release 0.98.1. The Supply Chain & Logistics Institute, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology Atlanta, USA.
• Stephens, M. P. (2019). Manufacturing facilities design & material handling (6th ed.). Purdue University Press.
• Tompkins, J. A., White, J. A., Bozer, Y. A., & Tanchoco, J. M. A. (2010). Facilities planning (4th ed.). J. Wiley & Sons.
Additional information on implementation and assessment Method (written or oral exam, coursework, project):
• Completed home-works 10%
• Project work 40%
• Written examination (theoretical and practical knowledge) 50%
