This year’s QBWL workshop was hosted on 8 March 2021 by the Chair of Supply and Value Chain Management at TUM Campus Straubing. Due to the COVID-19 pandemic, the workshop was hosted in a virtual format.
116 participants from 18 universities joined the workshop which consisted of a large number of presentations and discussions. In three streams on “Last Mile Delivery, Routing & Logistics”, “ Revenue Management & Supply Planning”, and “Sequencing & Scheduling”, 20 researchers presented their current or future research projects. The presentations were complemented by vivid discussions.The agenda can be found below.
QBWL is a gathering of German research groups working on quantitative topics in business and economics. It was established about 3 decades ago and has continuously grown to a group of more than 25 chairs. The workshop is hosted once a year by one of the participating chairs, usually as a 3-day in-person workshop.
Next year’s workshop is planned for March 28-31, 2022 in Kloster Drübeck, hosted by Prof. Sven Müller from the University of Magdeburg.
30th QBWL Workshop online (2021)
von QBWL Team am 29.10.2020, 14:35
The 30. QBWL Workshop takes place March 8. - 9. 2021. The format will be online.
For further information, see the "Workshop" section.
Aus der Forschung
Neural networks & Intensive care (J. Brunner)
von QBWL Team am 21.04.2021, 16:51
Die Belegung von Intensivstationen ist ein hochaktuelles Thema. Prof Dr. Jens Brunner (Universität Augsburg) und sein Team beschreiben im Artikel
Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks
wie die Planung von Operationen in Krankenhäusern mit Hilfe von neuronalen Netzen und mathematischer Optimierung effizienter gestaltet werden können. Der Artikel ist in Naval Research Logistics publiziert.
In a master surgery scheduling (MSS) problem, a hospital's operating room (OR) capacity is assigned to different medical specialties. This task is critical since the risk of assigning too much or too little OR time to a specialty is associated with overtime or deficit hours of the staff, deferral or delay of surgeries, and unsatisfied—or even endangered—patients. Most MSS approaches in the literature focus only on the OR while neglecting the impact on downstream units or reflect a simplified version of the real‐world situation. We present the first prediction model for the integrated OR scheduling problem based on machine learning. Our three‐step approach focuses on the intensive care unit (ICU) and reflects elective and urgent patients, inpatients and outpatients, and all possible paths through the hospital. We provide an empirical evaluation of our method with surgery data for Universitätsklinikum Augsburg, a German tertiary care hospital with 1700 beds. We show that our model outperforms a state‐of‐the‐art model by 43% in number of predicted beds. Our model can be used as supporting tool for hospital managers or incorporated in an optimization model. Eventually, we provide guidance to support hospital managers in scheduling surgeries more efficiently.
Schiele, J, Koperna, T, Brunner, JO. (2021) Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks, Naval Research Logistics 2021, 68: 65–88. DOI: 10.1002/nav.21929
In this work, we suggest concepts and solution methodologies for a series of strategic network design problems that find application in highly data-sensitive industries, such as, for instance, the high-tech, governmental, or military sector. Our focus is on the installation of widely used cost-efficient tree-structured communication infrastructure. As base model we use the well-known Steiner tree problem, in which we are given terminal nodes, optional Steiner nodes, and potential network links between nodes. Its objective is to connect all terminals to a distributor node using a tree of minimum total edge costs. The novel, practically relevant side constraints are related to privacy concerns of customers, represented by terminals. In order to account for these, we study four privacy models that restrict the eligible infrastructure for the customer-distributor data exchange: (I) Selected pairs of terminals mutually exclude themselves as intermediate data-transmission nodes; (II) some pairs of terminals require disjoint paths to the distributor; (III) individual terminals forbid routing their data through allegedly untrustworthy links; and (IV) certain terminals do not allow the usage of doubtful links on their entire network branch. These topological data-privacy requirements significantly complicate the notoriously hard optimization problem. We clarify the model relationships by establishing dominance results, point out potential extensions and derive reduction tests. We present corresponding, strong non-compact integer programming (IP) formulations and embed these in efficient cutting plane methods. In addition, we develop constraint programming formulations that are used complementally to derive primal solutions. In a computational study, we analyze the performance of our methods on a diverse set of literature-based test instances.
High-occupancy vehicle (HOV) lanes are restricted traffic lanes that are reserved for vehicles with multiple car occupants. Depending on the current number of passengers, a driver must either travel slower on the often-congested general-purpose lane or can access the faster HOV lane. In this paper, we provide optimization approaches for matching supply and demand when building carpools along HOV lanes. In current applications, carpools form spontaneously in slugging areas where potential passengers queue. However, internet-enabled mobile phones that are connected to a central ride sharing platform enable dynamic carpool formation based on sophisticated scheduling procedures. We investigate various versions of the carpool formation problem. The computational complexity is analyzed in depth, and suitable solution procedures are developed. These procedures are applied to quantify the benefit of an optimized carpool formation process. In a comprehensive computational study, we compare our optimization approaches with spontaneous ride sharing and show that substantially better solutions for all stakeholders can be obtained.
Nils Boysen, Dirk Briskorn, Stefan Schwerdfeger, Konrad Stephan (2021) Optimizing carpool formation along high-occupancy vehicle lanes, European Journal of Operational Research, DOI: 10.1016/j.ejor.2020.12.053
Online veröffentlicht: Januar 2021
Product allocation in retail stores (A. Hübner)
von QBWL Team am 12.04.2021, 15:11
Das Team von Prof. Dr. Alexander Hübner (TU München) veröffentlicht in diesem Jahr einen Forschungsbeitrag zur profitoptimierenden Gestaltung von Produktauslagen im Einzelhandel. Der Artikel mit dem Titel
Shelf space dimensioning and product allocation in retail stores
Retail shelves are adjustable by varying the number of shelf boards as well as the height and depth of each shelf board. Shelf planners adjust the boards accordingly at regular intervals when they create the shelf plans and allocate products. Current shelf planning models assume given shelf configurations and allocate only products. However, the dimensioning of a shelf segment and product allocation are interdependent. For instance, the height of one segment may be reduced if only small products are allocated or products cannot be stacked. This paper proposes the first integrated approach for shelf segment dimensioning and product allocation. It jointly determines the number of facings for each product, the shelf quantity and the size and number of shelf segments. We also identify and consider several restrictions for the shelf structure (e.g., technical options), allocation rules (e.g., maximum inventory reach) and allocation- and shelf-layout-dependent demand. We formulate the decision problem at hand which is an Integer Non-linear Program and apply a solution algorithm based on the application of bounds that are obtained by transferring constraints to a preprocessing stage. Doing so, we can reformulate the problem as Binary Integer Program, provide an exact approach and generate practical applicable and optimal solutions in a time-efficient manner. We show that integrating shelf dimensioning into product allocation results in up to 5% higher profits than benchmarks available in literature. By means of a case study we show how planning can be improved, and that the retailer’s profit margin can be improved by up to 7%.
Alexander Hübner, Tobias Düsterhöft, Manuel Ostermeier (2021) Shelf space dimensioning and product allocation in retail stores, European Journal of Operational Research, Volume 292, Issue 1, 2021,Pages 155-171, DOI: 10.1016/j.ejor.2020.10.030
Online veröffentlicht: November 2020
Healthcare facility location planning (K. Haase/S. Müller)
Preventive healthcare facility location planning with quality-conscious clients
einen Standortplanungsansatz, der das individuelle, nutzentheoretische Entscheidungsverhalten von Patienten bei der Wahl von Ärzten und Krankenhäusern in einem Optimierungsmodell der Standortplanung integriert. Der Artikel erschien im März 2021 in OR Spectrum.
Pursuing the overarching goal of saving both lives and healthcare costs, we introduce an approach to increase the expected participation in a preventive healthcare program, e.g., breast cancer screening. In contrast to sick people who need urgent medical attention, the clients in preventive healthcare decide whether to go to a specific facility (if this maximizes their utility) or not to take part in the program. We consider clients’ utility functions to include decision variables denoting the waiting time for an appointment and the quality of care. Both variables are defined as functions of a facility’s utilization. We employ a segmentation approach to formulate a mixed-integer linear program. Applying GAMS/CPLEX, we optimally solved instances with up to 400 demand nodes and 15 candidate locations based on both artificial data as well as in the context of a case study based on empirical data within one hour. We found that using a Benders decomposition of our problem decreases computational effort by more than 50%. We observe a nonlinear relationship between participation and the number of established facilities. The sensitivity analysis of the utility weights provides evidence on the optimal participation given a specific application (data set, empirical findings).