@phdthesis{Popp2015, author = {Popp, Andreas}, title = {Stochastische dynamische Losgr{\"o}ßenplanung mit positiven Bestellvorlaufzeiten}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:824-opus4-2543}, school = {Katholische Universit{\"a}t Eichst{\"a}tt-Ingolstadt}, pages = {VIII, 90 S. : graph. Darst.}, year = {2015}, abstract = {Die Losgr{\"o}ßenplanung bestimmt optimale Produktionsmengen unter Ber{\"u}cksichtigung von R{\"u}st- und Lagerhaltungskosten. In der stochastischen dynamischen Variante werden dabei dynamische Nachfragen in einem diskreten Periodenraster unterstellt, wobei von der Nachfrage (bis sie sich realisiert) lediglich die stochastische Verteilung bekannt ist. Die Realisierung erfolgt typischerweise in der gleichen Periode, in der die Nachfrage f{\"a}llig wird. Dies {\"a}ndert sich allerdings, wenn Kunden mit positiven Vorlaufzeiten bestellen. In diesem Fall erh{\"a}lt man bereits einige Zeit vor der Nachfrageperiode Informationen, welche bei der Entscheidung {\"u}ber die optimale Losgr{\"o}ße relevant sind. Diese Informationen heißen auch Advance Demand Information. In dieser Arbeit wird der Effekt von positiven Bestellvorlaufzeiten auf stochastische Losgr{\"o}ßenplanungsprobleme untersucht. Dabei wird zun{\"a}chst die Problemstellung detailliert analysiert. Ein Ergebnis dabei ist, dass sich positive Bestellvorlaufzeiten insbesondere bei Problemen unter der sogenannten statisch-dynamischen Unsicherheitsstrategie auswirken. Aus diesem Grund werden im Anschluss bestehende Ans{\"a}tze aus der Literatur f{\"u}r diese Problemklasse untersucht und bewertet. Danach werden daraus neue Ans{\"a}tze entwickelt, welche durch Ber{\"u}cksichtigung der Advance Demand Information bessere Ergebnisse erzielen k{\"o}nnen. Diese Ans{\"a}tze werden im folgenden Schritt schließlich einer numerischen Analyse unterzogen. Sie werden dabei anhand von aussagekr{\"a}ftigen Testdatens{\"a}tzen auf ihre Genauigkeit, ihre Kostenersparnis und ihren praktischen Nutzen hin untersucht. Die Arbeit endet mit einem Fazit und einem {\"U}berblick {\"u}ber offene Forschungsfragen.}, subject = {Lagerhaltung}, language = {de} } @phdthesis{Duesterhoeft2020, author = {D{\"u}sterh{\"o}ft, Tobias}, title = {Optimization models for shelf space allocation in retail stores [cumulative dissertation]}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:824-opus4-6323}, school = {Katholische Universit{\"a}t Eichst{\"a}tt-Ingolstadt}, year = {2020}, abstract = {The cumulative dissertation "Optimization models for shelf space allocation in retail stores" consists of the four individual scientific contributions listed below: 1. D{\"u}sterh{\"o}ft, Tobias, H{\"u}bner, Alexander and Schaal, Kai (2020): A practical approach to the shelf-space allocation and replenishment problem with heterogeneously sized shelves. 2. H{\"u}bner, Alexander, D{\"u}sterh{\"o}ft, Tobias and Ostermeier, Manuel (2020): An optimization approach for product allocation with integrated shelf space dimensioning in retail stores. 3. Ostermeier, Manuel, D{\"u}sterh{\"o}ft, Tobias and H{\"u}bner, Alexander (2020): A model for the store wide shelf space allocation. 4 D{\"u}sterh{\"o}ft, 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.}, subject = {Supply Chain Management}, language = {en} } @phdthesis{Schulz2023, author = {Schulz, Felix}, title = {Integrated models for the selection and weighting of individual opinions and forecasts in expectation and forecast combination}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:824-opus4-8478}, school = {Katholische Universit{\"a}t Eichst{\"a}tt-Ingolstadt}, year = {2023}, abstract = {In forecast combination, multiple predictions are linearly combined through the assignment of weights to individual forecast models or forecasters. Various approaches exist for defining the weights, which typically involve determining the number of models to be used for combination (selection), choosing an appropriate weighting function for the forecast scenario (weighting), and using regularization techniques to adjust the calculated weights (shrinkage). The papers listed address the integration of the three approaches into holistic data analytical models. The first paper develops a two-stage model in which weights are first calculated based on the in-sample error covariances to minimize the error on the available data by combining the individual models. Based on the selection status of a model, the weight of an individual model is then linearly shrunk either toward the mean or toward zero. The selection status is thereby derived apriori from information criteria, where Contribution 1 introduces the selection based on the model's in-sample accuracy and performance robustness under uncertainty. Contribution 2 modifies the two-stage model to shrink the forecasters' weights non-proportionally to the mean or zero. Further, a new information criterion based on forward feature selection is proposed that iteratively selects the forecaster that is expected to achieve the largest increase in accuracy when combined. Contribution 3 extends the iteration-based information criterion presented in the second contribution to consider diversity gains in addition to accuracy gains when selecting and combining forecasters. A one-stage model for simultaneous weighting, shrinking, and selection is finally built and evaluated on simulated data in Contribution 4. Instead of requiring a prior selection criterion, the model itself learns which forecasters to shrink to the mean or to zero, while relying on a new sampling procedure to tune the model.}, subject = {Prognoseverfahren}, language = {en} } @phdthesis{Walther2020, author = {Walther, Manuel}, title = {Bed planning: advanced applications of operational research in large hospitals}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:824-opus4-6441}, school = {Katholische Universit{\"a}t Eichst{\"a}tt-Ingolstadt}, pages = {xv, 161 Seiten, Seiten xvii-xxvii : Diagramme}, year = {2020}, abstract = {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 H{\"u}bner, A., Kuhn, H., Walther, M., 2018. Combining clinical departments and wards in maximum-care hospitals. OR Spectrum 40, 679-709 3 Sch{\"a}fer, F., Walther, M., H{\"u}bner, 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 Sch{\"a}fer, F., Walther, M., H{\"u}bner, 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.}, subject = {Krankenhaus}, language = {en} }