TY - THES A1 - Düsterhöft, Tobias T1 - Optimization models for shelf space allocation in retail stores [cumulative dissertation] N2 - The cumulative dissertation "Optimization models for shelf space allocation in retail stores" consists of the four individual scientific contributions listed below: 1. Düsterhöft, Tobias, Hübner, Alexander and Schaal, Kai (2020): A practical approach to the shelf-space allocation and replenishment problem with heterogeneously sized shelves. 2. Hübner, Alexander, Düsterhöft, Tobias and Ostermeier, Manuel (2020): An optimization approach for product allocation with integrated shelf space dimensioning in retail stores. 3. Ostermeier, Manuel, Düsterhöft, Tobias and Hübner, Alexander (2020): A model for the store wide shelf space allocation. 4 Düsterhöft, Tobias (2020): Retail shelf space planning - Differences, problems and opportunities of applied optimization models. The planning and best possible utilization of the available shelf space is of central importance for retailers. Shelf space is a scarce resource in stores today. Shelf planners need to determine optimal shelf spaces for each item within product allocation. Researchers have already developed several decision support models. Usually, these models have in common that they can make a decision on the number of facings per product. A facing is a visible sales unit of a product on the shelf. Behind a facing, depending on the depth of the shelf further sales units are located. With an increase in the number of facings the visibility of the product for customers is also increasing, which is associated with a certain demand effect, the so-called space elasticity. The content of this dissertation are optimization models that extend existing approaches to product allocation significantly and thus also enable a practical application of these approaches. Within the framework of a practical project substantial new contents for the product allocation can be determined. The resulting optimization models build on each other. Initially, in the 1st article the product allocation is extended by an exact consideration of the shelf space dimensions. The resulting question of the optimal shelf layout is the central part of the 2nd article. If the layouts of shelves are determined on the shelf, the total shelf space per category must be known beforehand. This question is dealt with in the 3rd article. Finally, new research fields are identified in the 4th article based on current real processes and requirements. N2 - Die kumulative Dissertation „Optimization models for shelf space allocation in retail stores“ (Optimierungsmodelle zur Regalplatzbelegung im Einzelhandel) setzt sich aus den nachfolgend genannten vier wissenschaftlichen Einzelbeiträgen zusammen: 1. Düsterhöft, Tobias, Hübner, Alexander und Schaal, Kai (2020): A practical approach to the shelf-space allocation and replenishment problem with heterogeneously sized shelves. 2. Hübner, Alexander, Düsterhöft, Tobias und Ostermeier, Manuel (2020): An optimization approach for product allocation with integrated shelf space dimensioning in retail stores. 3. Ostermeier, Manuel, Düsterhöft, Tobias und Hübner, Alexander (2020): A model for the store wide shelf space allocation. 4. Düsterhöft, Tobias (2020): Retail shelf space planning - Differences, problems and opportunities of applied optimization models. Die Planung und bestmögliche Ausnutzung des verfügbaren Regalplatzes ist von zentraler Bedeutung für Einzelhändler. Regalplatz ist in Filialen heutzutage eine knappe Ressource. Regalplaner müssen versuchen im Rahmen der Produktallokation - also der Zuweisung eines Produktes auf einen bestimmten Platz im Regal - optimale Entscheidungen zu treffen. Hierzu wurden von Forschern bereits mehrere Entscheidungsunterstützungsmodelle entwickelt. Diese haben in der Regel gemeinsam, dass sie eine Entscheidung über die Anzahl der Facings je Produkt liefern. Ein Facing ist eine sichtbare Verkaufseinheit eines Produktes im Regal. Hinter einem Facing können sich in Abhängigkeit der Regaltiefe noch weitere Verkaufseinheiten befinden. Mit einer Erhöhung der Facings wird also immer die Sichtbarkeit für den Kunden erhöht, was mit einer gewissen Steigerung der Nachfrage in Verbindung gebracht wird, der sogenannten Raumelastizität. Inhalt dieser Dissertation sind Optimierungsmodelle, die bestehende Ansätze zur Produktallokation signifikant erweitern und somit eine praktische Anwendbarkeit der Ergebnisse ermöglichen. Im Rahmen eines Praxisprojekts konnten wesentliche neue Inhalte für die Produktallokation ermittelt werden. Die resultierenden Optimierungsmodelle bauen aufeinander auf. Zunächst wird im 1. Beitrag die Produktallokation um eine exakte Berücksichtigung des Regalplatzes erweitert. Die sich daraus ergebende Frage des optimalen Regalaufbaus ist zentraler Teil des 2. Beitrages. Wenn die Einhängungen von Fachböden im Regal neu ermittelt werden, stellt sich simultan die Frage nach der optimalen Gesamtregalfläche je Kategorie. Diese Frage wird im 3. Beitrag behandelt. Schließlich werden im 4. Beitrag neue Forschungsfelder anhand aktueller Realprozesse ermittelt. T2 - Optimierungsmodelle zur Regalplatzbelegung im Einzelhandel [kumulative Dissertation] KW - Supply Chain Management KW - Logistik KW - Einzelhandel KW - Operations management KW - Retail operations KW - Shelf space planning Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:824-opus4-6323 ER - TY - THES A1 - Walther, Manuel T1 - Bed planning: advanced applications of operational research in large hospitals BT - [cumulative dissertation] N2 - In many countries today, a rising life expectancy and the associated demographic shift, coupled with the advancements of modern medicine, has fueled an ever-increasing cost pressure on healthcare systems. A driving factor for these rising costs can be seen in inpatient stays in hospitals that in many cases are connected to cost-intensive treatments. A central concern of any hospital management in such an environment is therefore to understand how to make the best possible use of available resources. A decisive factor in this regard is the management of bed capacities. The present cumulative dissertation comprises four contributions, which address open research questions in the field of strategic, tactical and operative bed planning: 1 Walther, M., 2020. Strategical, tactical, and operational aspects of bed planning problems in hospital environments. Submission planned to Social Science Research Network (SSRN) 2 Hübner, A., Kuhn, H., Walther, M., 2018. Combining clinical departments and wards in maximum-care hospitals. OR Spectrum 40, 679-709 3 Schäfer, F., Walther, M., Hübner, A., Kuhn, H., 2019. Operational patient-bed assignment problem in large hospital settings including overflow and uncertainty management. Flexible Services and Manufacturing Journal 31, 1012–1041 4 Schäfer, F., Walther, M., Hübner, A., Grimm, D., 2020. Machine learning and pilot method: tackling uncertainty in the operational patient-bed assignment problem. Submitted to OR Spectrum on 13 February 2020 The first contribution sets out to provide an overview over the different hierarchical planning levels on which bed planning problems may be addressed. It should be noted in this context that several different aspects may be combined under the collective term “bed planning”. These may be delimited in terms of their scope and their planning horizon. A frequently used taxonomy in this context is the hierarchical subdivision of typical problems in health care into strategical, tactical and operational levels as provided by Hulshof et al. (2012). In the context of bed planning, a typical strategical problem is how to combine departments and wards to obtain benefits from pooled ward capacity. On a tactical level, an exemplary problem setting related to bed planning can be seen in devising master surgery schedules that optimize downstream bed occupancy levels as patients returning from surgery will require a bed for post-surgical recovery and monitoring. Finally, on an operational level, patient-bed allocations need to be optimized while taking the objectives and constraints of patients and medical staff alike into account. To start, the second contribution deals with the strategical problem of combining departments into groups and assigning pooled ward capacity to these groups with the goal of balancing bed occupancy levels within a hospital. Specifically, one of the underlying goals is to minimize the amount of beds required to meet a predetermined service level. However, merging ward capacities with the aim of simultaneously accommodating patients from different medical departments increases the complexity of organizing and ensuring proper care for these patients. This leads to so-called pooling costs. To tackle this problem, a modeling and solution approach is developed which is based on a generalized partitioning problem and is solved by integer linear programming (ILP). This enables hospital management to determine the cost-optimal combination of all departments and wards in a hospital, while ensuring that predetermined thresholds with regard to maximum walking distances for doctors and patients are adhered to. Once pooled ward capacities are established, the solution space for allocating incoming patients to beds is greatly increased and the underlying allocation problem quickly becomes too complex to be handled without computational support. In this regard the third contribution ties in with the second contribution in that it deals with optimizing the operational patient-bed allocation problem. In order to enable optimal allocation of patients to beds, it is important to identify and take into account the individual needs and limitations of the three main stakeholders involved, namely patients, doctors, and nursing staff. All of these stakeholders exhibit different and sometimes contradicting objectives and constraints, such that a trade-off has to be made that maximizes the overall utility for the hospital. In addition, the complexity of the problem is increased by the high volatility and uncertainty regarding patient arrivals, types of illnesses, and the resulting remaining lengths of stay of newly arriving patients. In order to address this situation, a mathematical model and solution approach for the patient-bed allocation problem is developed that is designed to generate solutions for large, real-life operative planning situations. In addition to being able to deal with overflow situations, this solution approach further takes different patient types into account, for example by anticipating emergency patient arrivals. Finally, the fourth contribution builds on the third contribution in that the modeling and solution approach to allocate patients to beds is extended by several aspects. As mentioned above, hospitals have to deal with uncertainty regarding the actual demand for beds. Here, the fourth contribution improves the anticipation of emergency patients by using machine learning. Specifically, weather data, seasons, important local and regional events, and current and historical occupancy rates are combined to better anticipate emergency inpatient arrivals. In addition, a hyper-heuristic approach is developed based on the pilot method defined by Voß et al. (2005). By combining the improved anticipation of emergency patients with this hyperheuristic approach significant improvements can be achieved compared to the solution approach presented in the third contribution. KW - Krankenhaus KW - Operations Research KW - Gesundheitsfürsorge KW - Controlling KW - Bed Planning KW - Health Care KW - Optimization KW - Bed Occupancy Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:824-opus4-6441 ER -