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.
A model and solution approach for store-wide shelf space allocation
und wird im Juli 2021 offiziell erscheinen.
Store space is limited and one of the most costly resources of retailers. Retailers have to apportion available store space among the individual product categories of a store and therefore assign a certain share of shelf space to each category. Assigning more shelf space to one category requires reducing the number of shelves for another category as total space is limited. Reducing available shelf space in turn decreases assortment size and lessens the presentation quantity of products and vice versa. Both affect the demand of products and ultimately the profitability of the entire category such that the profit contribution of a category depends on its shelf size. This interrelation between category sizes and store profits needs to be taken into account for the shelf space assignment to categories and the space allocation for individual products. We introduce a store-wide shelf space model that optimizes shelf space assignment for categories based on the profit contribution of the corresponding product allocations. We decompose the problem into two hierarchically interlinked subproblems and show that the solution approach suggested works efficiently and provides solutions that are applicable to large problems in retail practice. In a case study with a major European retailer, we show that profits at stores can be improved by 3.2% using our approach. Further, we use simulated data to generalize the findings and derive managerial insights.
Recent advances in customer choice analysis demonstrated the strong impact of compromise alternatives on the behaviour of decision-makers in a wide range of decision situations. Compromise alternatives are characterized by an intermediate performance on some of the relevant attributes. For instance, price compromises are well known in the sense that customers tend to buy neither the cheapest, nor the most expensive alternative, but the mid-priced one. However, thus far, the literature on product line optimization has not considered such context effects.
In this paper, we propose a model-based approach for optimal product line selection which incorporates customers’ preferences for compromise alternatives. We consider customer choice in a realistic, sophisticated fashion by applying an established utility model that integrates compromise variables into a multinomial logit model. We formulate the resulting optimization problem as a mixed-integer linear program. The challenging feature for modelling – making the formulation substantially more complicated than existing ones without compromises – are the endogenous effects of selected products on other alternatives’ utilities that need to be adequately captured via compromise variables. Based on data we collected by a stated choice experiment in a retail setting, we perform a computational study and demonstrate the superiority of our product line selection approach compared to a reference model that does not take compromises into account. Even under uncertainty of the estimated utility parameters, profit gains of, on average, 23% can be achieved in our experimental setting.
Georg Bechler, Claudius Steinhardt, Jochen Mackert, Robert Klein (2021) Product line optimization in the presence of preferences for compromise alternatives, European Journal of Operational Research,Volume 288, Issue 3,2021,Pages 902-917, DOI: 10.1016/j.ejor.2020.06.029
Veröffentlicht: Februar 2021
Harvest planning in Thailand (R. Akkerman)
von QBWL Team am 27.04.2021, 11:09
In einer Gemeinschaftsarbeit mit einem thailändischen Wissenschaftsteam beschreibt Prof. Dr. Renzo Akkerman (Wageningen University) einen mehrkriteriellen Optimierungsansatz für die Zuckerrohrernte in Thailand. Der Artikel
A multi-objective approach to sugarcane harvest planning in Thailand: Balancing output maximization, grower equity, and supply chain efficiency
ist in der Zeitschrift Computers & Industrial Engineering erschienen und beschreibt das interessante Planungsproblem, auftretende Zielkonflikte sowie einen genetischen Algorithmus zur Lösung des Problems.
This paper addresses a multi-objective sugarcane harvesting problem in Thailand, where several conflicting objectives and local restrictions are regarded as major obstacles to a sustainable sugar production environment. A multi-objective modeling approach that balances three different objectives of different key supply chain actors, namely (i) maximizing output in terms of total sugar production volume, (ii) maximizing grower equity in terms of a fair harvesting time-slot distribution, and (iii) maximizing supply chain efficiency in terms of a lower variability in resource requirements across the harvesting season, is introduced and solved by a state-of-the-art multi-objective evolutionary genetic algorithm. To better help the algorithm generate efficient solutions forming the Pareto front, two local searches are also embedded and intermittently performed during algorithm execution. Based on the information of an operating mill in Kanchanaburi Province, Thailand, we have found that our approach produces solutions that are close to optimal in terms of production output. Nonetheless, by sacrificing a small amount of production output, these solutions provide significant improvements in terms of grower equity and supply chain resource efficiency, which are crucial for the survivability of involved actors.
Pisit Jarumaneeroj, Nutchanon Laosareewatthanakul, Renzo Akkerman (2021) A multi-objective approach to sugarcane harvest planning in Thailand: Balancing output maximization, grower equity, and supply chain efficiency, Computers & Industrial Engineering, Volume 154 (04/2021), DOI: 10.1016/j.cie.2021.107129
Veröffentlicht: Januar 2021
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.