040906 KU Selected Special Topics of Public and Non-Profit Management (MA) (2024W)
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 09.09.2024 09:00 to Th 19.09.2024 12:00
- Registration is open from We 25.09.2024 09:00 to Th 26.09.2024 12:00
- Deregistration possible until Mo 14.10.2024 23:59
Details
max. 50 participants
Language: German
Lecturers
Classes
Correct Dates:
Monday, 07.10.2024; 08.30- 12.30; Lecture hall 16, Oskar-Morgenstern-Platz 1, 2nd floor
Monday, 21.10.2024; 08.30- 12.30; Lecture hall 16, Oskar-Morgenstern-Platz 1, 2nd floor
Monday, 04.11.2024; 08.30- 12.30; Lecture hall 16, Oskar-Morgenstern-Platz 1, 2nd floor
Monday, 18.11.2024; 08.30- 12.30; Lecture hall 16, Oskar-Morgenstern-Platz 1, 2nd floor
Monday, 13.01.2025; 08.30- 12.30; Lecture hall 16, Oskar-Morgenstern-Platz 1, 2nd floor
Monday, 20.01.2025; 08.30- 12.30; Lecture hall 16, Oskar-Morgenstern-Platz 1, 2nd 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
All oral & written homework assignments have to follow scientific guidelines: https://pnpm.univie.ac.at/minormajorwahlfach-pnpm/).
• All files of the homeworks (oral and written part) must be electronically turned in a day before the presentation on the moodle platform (1 x doc-file or ppt-file, 1x pdf-file)
• The printed versions of the slides of the homeworks are part of the final grade and have to be turned in at the University of Vienna, OMP 1, porter, Mailbox Rauner latest on the day of the presentation). Please ONLY print one slide on ONE page on the front of the paper sheet! Please do NOT print on the back of the paper sheet!
• The quality of the printed version of the homeworks is part of the final grade.Presentation:
• Each presentation should take up to 30 minutes (10 minutes for small homeworks Ozkarahan/DEA and up to 20 minutes + 10 minutes discussion for the main homework on staff scheduling).
• There should be a short discussion with the auditorium after the presentation (5-10 minutes).
• The quality of the presentations is part of the final grade.Requirements for a positive grade (min. 50%):
• Positive participation in the blocked sessions (Moodle Quiz and Workshops: 35%).
• Elaboration and presentation of homework examples (lectures & slide sets: 65%).
• All oral and written assignments have to be positively evaluated.
• To pass the class, it is only allowed to miss not more than two blocked class sessions.
• The use of AI tools (e.g. ChatGPT) for the production of texts is only permitted if explicitly requested by the course instructor (e.g. for individual work tasks).
• All files will be checked for plagiarism!
• All files of the homeworks (oral and written part) must be electronically turned in a day before the presentation on the moodle platform (1 x doc-file or ppt-file, 1x pdf-file)
• The printed versions of the slides of the homeworks are part of the final grade and have to be turned in at the University of Vienna, OMP 1, porter, Mailbox Rauner latest on the day of the presentation). Please ONLY print one slide on ONE page on the front of the paper sheet! Please do NOT print on the back of the paper sheet!
• The quality of the printed version of the homeworks is part of the final grade.Presentation:
• Each presentation should take up to 30 minutes (10 minutes for small homeworks Ozkarahan/DEA and up to 20 minutes + 10 minutes discussion for the main homework on staff scheduling).
• There should be a short discussion with the auditorium after the presentation (5-10 minutes).
• The quality of the presentations is part of the final grade.Requirements for a positive grade (min. 50%):
• Positive participation in the blocked sessions (Moodle Quiz and Workshops: 35%).
• Elaboration and presentation of homework examples (lectures & slide sets: 65%).
• All oral and written assignments have to be positively evaluated.
• To pass the class, it is only allowed to miss not more than two blocked class sessions.
• The use of AI tools (e.g. ChatGPT) for the production of texts is only permitted if explicitly requested by the course instructor (e.g. for individual work tasks).
• All files will be checked for plagiarism!
Minimum requirements and assessment criteria
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
(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 materials will be provided 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 scheduling
• 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 scheduling
• 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 20.08.2024 15:45