Moses - Stochastic Models in Operations Management

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Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum WS 2015/16 - SS 2018.

Module / Version:
#70047 / #1

Validity:
WS 2015/16 - SS 2018

Default display language:
English

Module title:
Stochastic Models in Operations Management

Credits:
6

Responsible person:
Barz, Christiane

Grading:
benotet

Type of exam:
Schriftliche Prüfung

Zugehörigkeit

Faculty:
Fakultät VII

Institute:
Institut für Betriebswirtschaftslehre

Area of expertise:
37321200 FG Industrielles Produktions- und Dienstleistungsmanagement (POM)

Prüfungsausschuss:
Betriebswirtschaftslehre

Kontakt

Office:
ST 1-1

Contact person:
Barz, Christiane

E-mail address:
service@pm.tu-berlin.de

Website:

http://www.pm.tu-berlin.de

Learning Outcomes

Students can analyse simple stochastic problems by formulating them as decision trees and/or by using Monte-Carlo simulations. Students are able to model stochastic discrete-time processes adequately. They can characterize the long-term behavior of processes and know how to calculate desired system parameters if needed. Facing multiple choices at each stage, they can model the process as Markov decision-making process and find the best alternative (for a given objective function) by applying several suitable solution approaches. In the continous time case, students know the terminology of Markov processes and queuing systems. They can model the processes adequately, critically evaluate several modelling approaches, analyze the long-term behavior and calculate corresponding system parameters.

Content

– Decision Trees – Basics of Monte Carlo simulation – Markov chains (discrete time): Modeling, mathematical characteristics, applications – Stochastic decision processes: Formulation, solution methods – Markov processes (continuous time): Modeling, mathematical characteristics, applications – Queuing systems: Modeling, mathematical characteristics, calculation of system parameters, applications

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course Name	Type	Number	Cycle	Language	SWS	VZ
Stochastic Models in Production and Services Management	VL	72 120 L 8361	WiSe/SoSe	English	2
Stochastic Models in Production and Services Management	UE	72 120 L 8362	WiSe/SoSe	English	2

Workload and Credit Points

Stochastic Models in Production and Services Management (VL):

Workload description	Multiplier	Hours	Total
Class attendance	15.0	2.0h	30.0h
Class preperation and follow-up	15.0	2.0h	30.0h
Exam preparation	1.0	60.0h	60.0h
			120.0h(~4 LP)

Stochastic Models in Production and Services Management (UE):

Workload description	Multiplier	Hours	Total
Class attendance	15.0	2.0h	30.0h
Class preperation and follow-up	15.0	1.0h	15.0h
Preparation of homework	15.0	1.0h	15.0h
			60.0h(~2 LP)

The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

This module consists of a lecture with theory, practice and discussion elements. In the practice sessions, weekly homework and further exercises will be discussed. Some of the exercises will be solved by the help of software. Basic knowledge of Excel is helpful to gain a deeper understanding.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Statistik II für Wirtschaftswissenschaften

Mandatory requirements for the module test application:

1. Requirement

Modul70231 [Statistik I für Wirtschaftswissenschaften] passed

2. Requirement

Modul70146 [Operations Research - Grundlagen (OR-GDL)] passed

Module completion

Grading

graded

Type of exam

Written exam

Language

English

Duration/Extent

No information

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Winter- und Sommersemester.

Maximum Number of Participants

This module is not limited to a number of students.

Registration Procedures

none

Recommended literature
Puterman (2005). Markov Decision Processes: Discrete stochastic dynamic programming. Wiley.
Ross (2010). Introduction to Probability Models. Academic Press.
Waldmann, Stocker (2011). Stochastische Modelle: Eine anwendungsorientierte Einführung. Springer.

Assigned Degree Programs

Semester:

Zeige auch ausgelaufene Studiengänge und StuPOs

This module is used in the following Degree Programs (new System):

	Studiengang / StuPO	StuPOs	Verwendungen	Erste Verwendung	Letzte Verwendung
This module is not used in any degree program.

Students of other degrees can participate in this module without capacity testing.

Miscellaneous

You can get bonus points for the exam by successfully completing your homework.

Stochastic Models in Operations Management