Planning and analysis of strategic and operational service processes in the healthcare sector

The research of the chair deals with the planning and analysis of strategic and operational service processes in the health sector, especially in hospitals. The focus is on modelling, analyzing, and optimizing practice-relevant problems using empirical and quantitative/qualitative methods. The research work is carried out in close cooperation with practice partners. Central questions deal with the process, resource, quality, and information management. For example, staff scheduling of physicians and nurses as well as operating room scheduling are examined taking into account stochastic influences and dynamic scheduling. Furthermore, quality measurement and benchmarking of organizations and organizational units are supported by qualitative and quantitative analyses to improve core medical processes.

Our research activities are divided into three research areas, which are described in more detail on the following pages.


Resource Planning

In this research area, we deal with the optimal short-, medium- and long-term allocation of resources within medical processes. This includes duty and deployment planning in medical and nursing services as well as planning the training of future physicians. In addition, we deal with questions of operating room planning, including the preceding emergency room, following intensive care capacities, and the scheduling of appointments afterward.

Data Analytics

By working on issues in the field of data analytics, the chair tries to apply methods of operations research and management science, especially but not exclusively in the healthcare sector, to analyze existing data and derive appropriate recommendations for action. These data originate, for example, from hospital information systems and, depending on the application, can include occupancy and treatment data or even working hours and procedure durations.

Medical Decision Making

Appropriate mathematical-statistical methods such as machine learning algorithms, classical prediction methods, or simulation models are used to support the decision-making of the physician or healthcare provider in various areas. For example, machine learning models based on standard laboratory parameters are used to predict a corona diagnosis as quickly as possible.

Literature Reviews

Providing the first literature review on physician scheduling, we present an overview focusing on problem specific features, e.g., type of problem, fairness considerations, and applied shift types, as well as quantitative solution approaches. We identify a total of 68 papers on physician scheduling from 1985, when the first paper was published, until the end of 2016 in the field of operations research and management science. Additionally, we conduct a descriptive analysis to discuss developments in physician scheduling literature in general, e.g., trends in the geographical background and the number of publications. Based on our analysis and the discussion of various cross-sectional and interaction effects, some promising directions for future research such as the implementation of breaks and flexibility in the scheduling process as well as the consideration of uncertainty and stochasticity in physician scheduling are identified.


Erhard, M., Schoenfelder, J., Fügener, A., Brunner, J.O. (2018). State of the art in physician scheduling. European Journal of Operational Research, 265(1), 1-18.


Link to tables


With our publication “The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals” (Kohl et al., 2018) we provide an exhaustive literature overview in the field of DEA in healthcare. The paper reviews 262 publications of DEA applications in healthcare with special focus on hospitals and therefore closes a gap of over ten years (2005-2016) that were not covered by existing review articles. A variety of descriptive statistics on authors, countries and research questions are provided. Furthermore, our paper contains a description and analysis of the used input and output settings, the model specifications and the utilization of downstream techniques. Beyond the mere review part, the paper works as a roadmap to important methodological literature and publications, which provide crucial information on the setup of DEA studies. Finally, we discuss what could be done to advance DEA from a scientific tool to an instrument that is actually utilized by managers and policymakers.


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, 1-42.


The main tables of the publication (table 5 and 6), as well as additional information on the model usage of all reviewed papers are summarized in the following database.

Link to tables


We present the state-of-the-art of research on material logistics management in hospitals. Particular focus is given to articles that apply quantitative methods. Our contribution is threefold: First, we provide research guidance through categorizing literature and identifying major research streams. Second, we discuss applied methodologies and third, we identify future research directions. A systematic approach is undertaken in order to identify the relevant literature from 1998 to 2014. Applicable publications are categorized thematically and methodologically and future research opportunities are worked out. In total, 145 publications are identified and discussed in this work. The literature is categorized into four streams, i.e., (1) Supply and procurement, (2) Inventory management, (3) Distribution and scheduling, and (4) Holistic supply chain management. The use of optimization techniques is constantly gaining importance. The number of respective publications has continually grown and has peaked over the last three years. Optimization has been successfully applied in research streams (1), (2), and (3). Category (4) comprises a rather qualitative research field of literature dealing with supply chain management issues.


Volland, J., Fügener, A., Schoenfelder, J., Brunner, J. O. (2017). Material logistics in hospitals: A literature review. Omega, vol. 69, pp. 82-101.


The intensive care unit (ICU) is a crucial and expensive resource largely affected by uncertainty and variability. Insufficient ICU capacity causes many negative effects not only in the ICU itself, but also in other connected departments along the patient care path. Operations research/management science (OR/MS) plays an important role in identifying ways to manage ICU capacities efficiently and in ensuring desired levels of service quality. As a consequence, numerous papers on the topic exist. The goal of this paper is to provide the first structured literature review on how OR/MS may support ICU management. We start our review by illustrating the important role the ICU plays in the hospital patient flow. Then we focus on the ICU management problem (single department management problem) and classify the literature from multiple angles, including decision horizons, problem settings, and modeling and solution techniques. Based on the classification logic, research gaps and opportunities are highlighted, e.g., combining bed capacity planning and personnel scheduling, modeling uncertainty with non-homogenous distribution functions, and exploring more efficient solution approaches.


Bai, J., Fügener, A., Schoenfelder, J., Brunner, J. O. (2018). Operations research in intensive care unit management: a literature review. Health Care Management Science, 21(1), 1-24.


The case mix planning problem deals with choosing the ideal composition and volume of patients in a hospital. With many countries having recently changed to systems where hospitals are reimbursed for patients according to their diagnosis, case mix planning has become an important tool in strategic and tactical hospital planning. Selecting patients in such a payment system can have a significant impact on a hospital’s revenue. The contribution of this article is to provide the first literature review focusing on the case mix planning problem. We describe the problem, distinguish it from similar planning problems, and evaluate the existing literature with regard to problem structure and managerial impact. Further, we identify gaps in the literature. We hope to foster research in the field of case mix planning, which only lately has received growing attention despite its fundamental economic impact on hospitals.


Hof, S., Fügener, A., Schoenfelder, J., Brunner, J.O. (2017). Case Mix Planning in Hospitals: A Review and Future Agenda. Health Care Management Science, 20(2), 207-220.



The workshop was initiated by Prof. Dr. Katja Schimmelpfeng (University of Hohenheim), Prof. Dr. Andreas Fügener (University of Cologne), Jun.-Prof. Alexander Hübner (University of Eichstätt-Ingolstadt) and Prof. Dr. Jens Brunner (University of Augsburg). The workshop will focus on the transformation of research projects into practice. The workshop takes place at regular intervals at a local cooperation partner.

Since 2004, a workshop has been held twice a year as part of the Graduate Program in Operations Management in Manufacturing, Logistics, and Services (GPOM). Professors and staff from the Ingolstadt School of Management, TU München, UniBw München, and the University of Augsburg are involved and take turns in hosting the workshop.

The annual Quantitative Business Administration (QBWL) workshop is organized and hosted alternately by professors and staff of the participating institutions. These are the Technical University of Munich, Bergische University of Wuppertal, Helmut Schmidt University of Hamburg, University of Hamburg, University of Mannheim, the University of Duisburg-Essen, Catholic University of Eichstätt-Ingolstadt, University of Hohenheim, Karlsruhe University of Applied Sciences, University of Siegen, Chemnitz University of Technology, Friedrich Schiller University of Jena, University of the Federal Armed Forces Munich, University of Cologne, and the University of Augsburg.