Cluster Events

Dear Professors,

The OMOR Cluster and the CERESSEC Research Center have the pleasure to invite you to our next seminar with 

Stephan NICKEL - KIT

On Thursday, April 2 from 12pm to 1pm

Room : N406

Title: Emergency Logistics in Healthcare

Stefan Nickel, KIT, Karlsruhe, Germany

Institute of Operations Research

Discrete Optimization and Logistics (dol.ior.kit.edu)

Executive Director FZI (www.fzi.de/LSO)

Director KSRI (ksri.kit.edu)

stefan.nickel@kit.edu

Abstract: Healthcare involves both medical and logistical activities. While medical professionals are responsible for designing medical aspects, Operations Research (OR) can offer quantitative decision support for designing logistical processes. The role of logistics is especially pronounced in (prehospital) emergency care, where the time until treatment can have a crucial impact on the patient outcome. Prehospital emergency care is provided by Emergency Medical Services (EMS) which face a variety of planning problems that can be addressed with OR methods. On the operational planning level, the dispatching decision determines which ambulance to send to an emergency – for instance, whether it should always be the closest one – while relocation strategies locate ambulances dynamically.

Tactical decisions include the allocation and shift planning for ambulances as well as for ambulance crews. Coordination centres in EMS need to create shift schedules and assign their call-takers and dispatchers to shifts considering availability requirements for fluctuating call volumes, legal requirements such as maximum shift lengths or rest days as well as staff preferences regarding ride sharing or common lunch breaks. On the strategic planning level, decisions must be made regarding the location of ambulance stations and the design of EMS districts. These planning problems can be addressed by a combination of queuing theory, mathematical programming and simulation, which can be combined with machine learning techniques, for instance to improve the parameterization of models. A crucial yet often overlooked step in applying quantitative models in healthcare is ensuring that the chosen objective criteria genuinely contribute to enhancing patient care. Drawing on an applied project focused on EMS, this talk will showcase planning problems and corresponding modelling approaches in emergency logistics illustrating how OR can make a tangible impact and contribute to policy changes. Further information about our ongoing research and projects can be found here.

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Daniel KUHN - EPFL 

On Tuesday, April 21 from 12pm to 1pm

Room : N406

Title: On Robust Optimization, Blackouts and the Law

Abstract: Vehicle-to-grid is a concept for mitigating the growing storage demand of electricity grids by using the batteries of parked electric vehicles for providing frequency regulation. Vehicles owners offering frequency regulation promise to charge or discharge their batteries whenever the grid frequency deviates from its nominal value, and they must be able to honor their promises for all frequency deviation trajectories that satisfy certain properties prescribed by EU law. We show that the relevant EU regulations can be encoded exactly in a robust optimization model, and we use this model to demonstrate that the penalties for non-compliance with market rules are currently too low. This suggests that “crime pays” and that the stability of the electricity grid is jeopardized if many frequency providers abuse the system, which could ultimately result in blackouts. The decision problem of a vehicle owner constitutes a non-convex robust optimization problem affected by functional uncertainties. By exploiting the structure of the uncertainty set and exact linear decision rules, however, we can prove that this problem is equivalent to a tractable linear program. Through numerical experiments based on data from France, we quantify the economic value of vehicle-to-grid and elucidate the financial incentives of vehicle owners, aggregators, equipment manufacturers, and regulators. The proposed robust optimization model is relevant for a range of applications involving energy storage.

If you need more information, please contact matta@essec.edu.

Best regards,