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Event
Mathematical models and methods for Production Systems [WS202117059]
Organisation
- KIT-Fakultät für Maschinenbau
Part of
Literature
Wolff: Stochastic Modeling and the Theory of Queues, Prentice Hall, 1989
Shanthikumar, Buzacott: Stochastic Models of Manufacturing Systems
Appointments
- 05.11.2020 14:00 - 17:30 - Room: 50.38 Raum 0.22
- 12.11.2020 14:00 - 17:30 - Room: 50.38 Raum 0.22
- 19.11.2020 14:00 - 17:30 - Room: 50.38 Raum 0.22
- 26.11.2020 14:00 - 17:30 - Room: 50.38 Raum 0.22
- 03.12.2020 14:00 - 17:30 - Room: 50.38 Raum 0.22
- 10.12.2020 14:00 - 17:30 - Room: 50.38 Raum 0.22
- 17.12.2020 14:00 - 17:30 - Room: 50.38 Raum 0.22
- 07.01.2021 14:00 - 17:30 - Room: 50.38 Raum 0.22
- 14.01.2021 14:00 - 17:30 - Room: 50.38 Raum 0.22
- 21.01.2021 14:00 - 17:30 - Room: 50.38 Raum 0.22
- 28.01.2021 14:00 - 17:30 - Room: 50.38 Raum 0.22
- 04.02.2021 14:00 - 17:30 - Room: 50.38 Raum 0.22
- 11.02.2021 14:00 - 17:30 - Room: 50.38 Raum 0.22
- 18.02.2021 14:00 - 17:30 - Room: 50.38 Raum 0.22
Note
Media:
black board, lecture notes, presentations
Learning Content:
- Single server systems: M/M/1, M/G/1: priority rules, model of failures
- Networks: open and closed approximations, exact solutions and approximations
- Application to flexible manufacturing systems, AGV (automated guided vehicles) - systems
- Modeling of control approaches like constant work in process (ConWIP) or kanban
- Discrete-time modeling of queuing systems
Learning Goals:
Students are able to:
- Describe queueing systems with analytical solvable stochastic models,
- Derive approches for modeling and controlling material flow and production systems based on models of queueing theory,
- Use simulation and exakt methods.
Recommendations:
- Basic knowledge of statistic
- Recommended lecture: Materials flow in logistic systems (also parallel)
Registration information:
This lecture has a restricted number of participants. Further information for registration and deadlines can be found on the website of the institute.
Workload:
regular attendance: 42 hours
self-study: 198 hours