040906 KU Selected Special Topics of Public and Non-Profit Management (MA) (2025W)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 08.09.2025 09:00 to We 17.09.2025 12:00
- Registration is open from We 24.09.2025 09:00 to Th 25.09.2025 12:00
- Deregistration possible until Tu 14.10.2025 23:59
Details
max. 50 participants
Language: German
Lecturers
Classes
1) Wednesday, October 8th, 2025, 12.30 pm to 4.30 pm; Oskar-Morgenstern-Platz 1, Seminar Room 15, 3rd floor
2) Wednesday, October 15th, 2025, 12.30 pm to 4.30 pm; Oskar-Morgenstern-Platz 1, Seminar Room 15, 3rd floor
3) Wednesday, October 22nd, 2025, 12.30 pm to 4.30 pm; Oskar-Morgenstern-Platz 1, Seminar Room 15, 3rd floor
4) Wednesday, October 29th, 2025, 12.30 pm to 4.30 pm; Oskar-Morgenstern-Platz 1, Seminar Room 15, 3rd floor
5) Wednesday, November 5th, 2025, 12.30 pm to 4.30 pm; Oskar-Morgenstern-Platz 1, Seminar Room 15, 3rd floor
6) Wednesday, December 3rd, 2025, 12.30 pm to 4.30 pm; Oskar-Morgenstern-Platz 1, Seminar Room 15, 3rd floor
Information
Aims, contents and method of the course
http://pnpm.univie.ac.at/minormajor-pnpm/At the beginning of the course, we will introduce the method of linear optimization and demonstrate the application area of resource scheduling in public and non-profit management (PNPM).The first part of the course on service provision in PNPM focuses on staff scheduling and is illustrated using an example from the healthcare sector with nurses and doctors. Next, we discuss models for nurse scheduling based on the literature. Finally, students solve nurse scheduling problems related to international literature. The students critically evaluate applied staff scheduling systems especially in the health care, disaster management, and education sectors as well as other areas of PNPM.In the second part of this course, we apply benchmarking approaches to evaluate an optimal resource allocation (e.g., staff, equipment, capital) in Decision Making Units (DMUs) in PNPM (e.g., medical organizations, schools/universities, disaster management, environment, energy, sports/art/culture). We explain the method of Data Envelopment Analysis (DEA) for benchmarking which is then applied on several examples together with the students.To promote connection to the practice, there will be an opportunity for an excursion to policymakers or to attend scientific talks.
Assessment and permitted materials
Presentation slide sets:
• Follow the guidelines for literature research, slide sets, written elaborations and AI tools (see: https:\\pnpm.univie.ac.at).
• Submission of the workshop/homework (part presentation - one-page printout with max. two slides per page in a transparent envelope to be handed in before the presentation in the lecture hall & Moodle submission of the .pdf files and .ppt files in the morning before the presentation).
• The quality of the slide set will be included in the assessment.
• Please note: all written papers are subject to a plagiarism check (software etc.). Research should be carried out independently and texts should be written by the students themselves - only refinement (correction of spelling/grammar errors) using artificial intelligence is permitted. Otherwise a negative assessment will be given.
• Permitted AI tools for language refinement: DeepL, Grammarly, and ScribbrOral presentation:
• The presentation of a homework topic should last max. 30 minutes (examples Ozkarahan/DEA - depending on the questions taking 5-10 minutes per person).
• The presentation is followed by a discussion (max. 5-10 minutes) with the auditorium.
• The impression of the presentation is part of the assessment.
• Follow the guidelines for literature research, slide sets, written elaborations and AI tools (see: https:\\pnpm.univie.ac.at).
• Submission of the workshop/homework (part presentation - one-page printout with max. two slides per page in a transparent envelope to be handed in before the presentation in the lecture hall & Moodle submission of the .pdf files and .ppt files in the morning before the presentation).
• The quality of the slide set will be included in the assessment.
• Please note: all written papers are subject to a plagiarism check (software etc.). Research should be carried out independently and texts should be written by the students themselves - only refinement (correction of spelling/grammar errors) using artificial intelligence is permitted. Otherwise a negative assessment will be given.
• Permitted AI tools for language refinement: DeepL, Grammarly, and ScribbrOral presentation:
• The presentation of a homework topic should last max. 30 minutes (examples Ozkarahan/DEA - depending on the questions taking 5-10 minutes per person).
• The presentation is followed by a discussion (max. 5-10 minutes) with the auditorium.
• The impression of the presentation is part of the assessment.
Minimum requirements and assessment criteria
Requirements for passing the course:
• Participation in the blocked exercise units (Moodle quiz & workshops: 35%).
• Preparation and presentation of homework examples (lectures & slide sets: 65%).
• The use of AI tools (e.g., ChatGPT) for the production of texts is only permitted if this is expressly requested by the course instructor (e.g., for individual assignments).
• Two absences from block units are possible, beyond that the course cannot be passed!
• Please note: all written assignments are subject to a plagiarism check (software etc.). Research should be carried out independently and texts should be written by the students themselves - only refinement (correction of spelling/grammar errors) using artificial intelligence is permitted. Otherwise a negative assessment will be awarded.Attendance/Participation in units: max. 25%
(Moodle Quiz = 4 x 4% = 16%; Participation = 9%)
Participation Workshops LP, Staff Scheduling, DEA: max.10%
Presentation Homework Ozkarahan: max. 10%
Slides Homework Ozkarahan: max. 22,5%
Presentation Homework DEA: max. 10%
Slides Homework DEA: max. 22,5%"1": 90%-100%
"2": 80%-89,75%
"3": 66%-79,75%
"4": 50%-65,75%
"5": <49,75% and/or missing in more than two block class sessions or plagiarism of homework paper
• Participation in the blocked exercise units (Moodle quiz & workshops: 35%).
• Preparation and presentation of homework examples (lectures & slide sets: 65%).
• The use of AI tools (e.g., ChatGPT) for the production of texts is only permitted if this is expressly requested by the course instructor (e.g., for individual assignments).
• Two absences from block units are possible, beyond that the course cannot be passed!
• Please note: all written assignments are subject to a plagiarism check (software etc.). Research should be carried out independently and texts should be written by the students themselves - only refinement (correction of spelling/grammar errors) using artificial intelligence is permitted. Otherwise a negative assessment will be awarded.Attendance/Participation in units: max. 25%
(Moodle Quiz = 4 x 4% = 16%; Participation = 9%)
Participation Workshops LP, Staff Scheduling, DEA: max.10%
Presentation Homework Ozkarahan: max. 10%
Slides Homework Ozkarahan: max. 22,5%
Presentation Homework DEA: max. 10%
Slides Homework DEA: max. 22,5%"1": 90%-100%
"2": 80%-89,75%
"3": 66%-79,75%
"4": 50%-65,75%
"5": <49,75% and/or missing in more than two block class sessions or plagiarism of homework paper
Examination topics
See literature
Reading list
All relevant course materials will be made available on the e-learning-platform Moodle.Main literature:Linear Optimization
• Günther, H.-O. (1993) Produktionsmanagement, Einführung mit Übungsaufgaben, Heidelberg.
• Hillier, F.S., Lieberman, G.J. (1988) Operations Research, Einführung, München.Nurse Scheduling
• Van den Bergh, J., Beliën, J., De Bruecker, P., Demeulemeester, E., & De Boeck, L. (2013) Personnel scheduling: A literature review, European Journal of Operational Research, Vol. 226, No. 3, pp. 367-385.
• Brucker, P., Qu, R., & Burke, E. (2011) Personnel scheduling: Models and complexity, European Journal of Operational Research, Vol. 210, No. 3, pp. 467-473.
• De Causmaecker, P., & Vanden Berghe, G. (2011) A categorisation of nurse rostering problems, Journal of Scheduling, Vol. 14 No. 1, pp. 3-16.
• Burke, E.K., De Causmaecker, P., Vanden Berghe, G., Van Landegheme, H. (2004) The state of the art of nurse roster-ing, Journal of Scheduling, Vol. 7, No. 6, pp. 441-499.
• Gutjahr, W.J, Rauner, M.S. (2007) An ACO algorithm for a dynamic regional nurse-scheduling problem in Austria, Computers & Operations Research, Vol. 41, No. 3, pp. 642-666.
• Erhard, M., Schoenfelder, J., Fügener, A., & Brunner, J. O. (2018). State of the art in physician scheduling. European Journal of Operational Research, Vol. 265, No. 1, 1-18Nurse Scheduling (Homework Ozkarahan)
• Ozkarahan, I., A Disaggregation Model of a Flexible Nurse Scheduling Support System (1991) Socio-Economic Planning Sciences, Vol. 25, No. 1, pp. 9-26.
• Rauner, M.S. (1999) A note on “A disaggregation model of a flexible nurse scheduling support system” by Irem Ozkarahan, Socio-Economic Planning Sciences 25 (1991), pp. 9-26”, Socio-Economic Planning Sciences, Vol. 33, No. 2, pp.173-177.DEA
• Ozcan, Y. (2008) Health Care Benchmarking and Performance Evaluation, An Assessment using Data Envelopment Analysis (DEA), Springer, New York.
• O'Neill, L., Rauner, M.S., Heidenberger, K., Kraus, M. (2008) A cross-national comparison and taxonomy of hospital efficiency studies using data envelopment analysis, Socio-Economic Planning Sciences, Vol. 42, No. 3, p. 158-189.
• Cooper, W.W., Seiford, L.M., Zhu, J. (2004) Handbook on Data Envelopment Analysis, Kluwer, Boston.
• Charnes, A., Cooper, W.W., Rhodes, E. (1978) Measuring the efficiency of decision making units, European Journal of Operational Research, Vol. 2, p. 429-444.
• Kohl, S., Schoenfelder, J., Fügener, A., & Brunner, J. O. (2018). The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals. Health Care Management Science, 1-43.
• Meyer, M., Wohlmannstetter, V. (1985) Effizienzmessing in Krankenhäusern, Zeitschrift für Betriebswirtschaft, Vol. 55, p. 262-280.
• Bürkle, B. (1994) Data Envelopment Analysis, Zeitschrift für öffentliche und gemeinwirtschaftliche Unternehmen, Vol. 17, p. 273-291.
• Sommersguter-Reichmann, M., & Stepan, A. (2015). The interplay between regulation and efficiency: Evidence from the Austrian hospital inpatient sector. Socio-Economic Planning Sciences, 52, 10-21.DEA (Homework DEA)
• Dyson, R.G., Allen, R. Camanho, A.S., Podinovski, V.V., Sarrico C.S., Shale E. A. (2001) Pitfalls and protocols in DEA, European Journal of Operational Research, Vol. 132, No. 2, p. 245-259.Staff planning
• Von Eiff, W., Stachel, K. (2006) Professionelles Personalmanagement: Erkenntnisse und Best-Practice-Empfehlungen für Führungskräfte im Gesundheitswesen, Münster.
• Grütz, M. (1984) Computerunterstützte Personalbedarfsermittlung für den Krankenpflegebereich auf der Grundlage von Dienstzeitplanungen, Nürnberg.
• Trill, R. (2002) Krankenhaus-Management: Aktionsfelder und Erfolgspotentiale, Neuwied.
• Günther, H.-O. (1993) Produktionsmanagement, Einführung mit Übungsaufgaben, Heidelberg.
• Hillier, F.S., Lieberman, G.J. (1988) Operations Research, Einführung, München.Nurse Scheduling
• Van den Bergh, J., Beliën, J., De Bruecker, P., Demeulemeester, E., & De Boeck, L. (2013) Personnel scheduling: A literature review, European Journal of Operational Research, Vol. 226, No. 3, pp. 367-385.
• Brucker, P., Qu, R., & Burke, E. (2011) Personnel scheduling: Models and complexity, European Journal of Operational Research, Vol. 210, No. 3, pp. 467-473.
• De Causmaecker, P., & Vanden Berghe, G. (2011) A categorisation of nurse rostering problems, Journal of Scheduling, Vol. 14 No. 1, pp. 3-16.
• Burke, E.K., De Causmaecker, P., Vanden Berghe, G., Van Landegheme, H. (2004) The state of the art of nurse roster-ing, Journal of Scheduling, Vol. 7, No. 6, pp. 441-499.
• Gutjahr, W.J, Rauner, M.S. (2007) An ACO algorithm for a dynamic regional nurse-scheduling problem in Austria, Computers & Operations Research, Vol. 41, No. 3, pp. 642-666.
• Erhard, M., Schoenfelder, J., Fügener, A., & Brunner, J. O. (2018). State of the art in physician scheduling. European Journal of Operational Research, Vol. 265, No. 1, 1-18Nurse Scheduling (Homework Ozkarahan)
• Ozkarahan, I., A Disaggregation Model of a Flexible Nurse Scheduling Support System (1991) Socio-Economic Planning Sciences, Vol. 25, No. 1, pp. 9-26.
• Rauner, M.S. (1999) A note on “A disaggregation model of a flexible nurse scheduling support system” by Irem Ozkarahan, Socio-Economic Planning Sciences 25 (1991), pp. 9-26”, Socio-Economic Planning Sciences, Vol. 33, No. 2, pp.173-177.DEA
• Ozcan, Y. (2008) Health Care Benchmarking and Performance Evaluation, An Assessment using Data Envelopment Analysis (DEA), Springer, New York.
• O'Neill, L., Rauner, M.S., Heidenberger, K., Kraus, M. (2008) A cross-national comparison and taxonomy of hospital efficiency studies using data envelopment analysis, Socio-Economic Planning Sciences, Vol. 42, No. 3, p. 158-189.
• Cooper, W.W., Seiford, L.M., Zhu, J. (2004) Handbook on Data Envelopment Analysis, Kluwer, Boston.
• Charnes, A., Cooper, W.W., Rhodes, E. (1978) Measuring the efficiency of decision making units, European Journal of Operational Research, Vol. 2, p. 429-444.
• Kohl, S., Schoenfelder, J., Fügener, A., & Brunner, J. O. (2018). The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals. Health Care Management Science, 1-43.
• Meyer, M., Wohlmannstetter, V. (1985) Effizienzmessing in Krankenhäusern, Zeitschrift für Betriebswirtschaft, Vol. 55, p. 262-280.
• Bürkle, B. (1994) Data Envelopment Analysis, Zeitschrift für öffentliche und gemeinwirtschaftliche Unternehmen, Vol. 17, p. 273-291.
• Sommersguter-Reichmann, M., & Stepan, A. (2015). The interplay between regulation and efficiency: Evidence from the Austrian hospital inpatient sector. Socio-Economic Planning Sciences, 52, 10-21.DEA (Homework DEA)
• Dyson, R.G., Allen, R. Camanho, A.S., Podinovski, V.V., Sarrico C.S., Shale E. A. (2001) Pitfalls and protocols in DEA, European Journal of Operational Research, Vol. 132, No. 2, p. 245-259.Staff planning
• Von Eiff, W., Stachel, K. (2006) Professionelles Personalmanagement: Erkenntnisse und Best-Practice-Empfehlungen für Führungskräfte im Gesundheitswesen, Münster.
• Grütz, M. (1984) Computerunterstützte Personalbedarfsermittlung für den Krankenpflegebereich auf der Grundlage von Dienstzeitplanungen, Nürnberg.
• Trill, R. (2002) Krankenhaus-Management: Aktionsfelder und Erfolgspotentiale, Neuwied.
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Last modified: Tu 12.08.2025 11:45