Dagstuhl Seminar 25121
Scheduling
( Mar 16 – Mar 21, 2025 )
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Organizers
- Claire Mathieu (CNRS - Paris, FR)
- Nicole Megow (Universität Bremen, DE)
- Benjamin J. Moseley (Carnegie Mellon University - Pittsburgh, US)
- Frits C. R. Spieksma (TU Eindhoven, NL)
Contact
- Andreas Dolzmann (for scientific matters)
- Christina Schwarz (for administrative matters)
Dagstuhl Reports
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At this Dagstuhl Seminar, we propose to focus on the established and emerging models for fairness in scheduling and resource allocation. The seminar will bring algorithmic scheduling researchers who traditionally consider scheduling and resource allocation to algorithmically optimize efficiency, without fairness considerations, together with researchers who model fairness and consider fairness allocation.
The seminar will focus on four complementary themes in fairness and resource allocation. The seminar will bring together researchers working on distinct areas to encourage cross-fertilization among different research directions. Moreover, these themes have sufficient overlap between them that it will be natural for participants to find common research directions.
Fair Allocation: Fair allocation has taken center stage in multi-agent systems and economics over the past decade due to its significance both industrially and socially. Essentially, it addresses how to distribute items, whether they be goods or tasks, to agents in a way that leaves each content with their share.
Balancing Fairness and Quality of Service: In the algorithms community, striking a balance between fairness and quality of service (QoS) is a pressing concern. While algorithms, particularly in sectors like finance, healthcare, and social networking, play a pivotal role in decision-making, ensuring equitable outcomes without compromising efficiency or performance is challenging. Fairness ensures that no group or individual is unfairly disadvantaged or discriminated against by algorithmic decisions, and it aims to create an even playing field across diverse sets of users or stakeholders.
Modeling Fairness: Modeling fairness in scheduling and resource allocation presents a plethora of challenges. Scheduling and allocating resources inherently involves making decisions that prioritize certain tasks, individuals, or groups over others, which can inadvertently introduce biases or create disparities. One fundamental challenge lies in defining what “fairness” actually means in varied contexts, as it can be subjective and differ across stakeholders.
Fairness in Tournament Design: Another theme where fairness and scheduling come together is in the design of tournaments. Tournaments are universally used mechanisms to rank alternatives; applications range from making hiring decisions to identifying winners in sport contests. Increased societal attention for fairness in these applications has brought about increased scrutiny over the existing rules that govern such tournaments. It has become clear that even in “simple” (and popular) formats such as a round robin, or a knockout tournament, fairness is a multi-faceted issue. Scheduling procedures used in these tournaments have a direct influence on the aspects determining fairness.
Creative Commons BY 4.0
Claire Mathieu, Nicole Megow, Benjamin J. Moseley, and Frits C. R. Spieksma
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- Antonios Antoniadis (University of Twente, NL) [dblp]
- Yossi Azar (Tel Aviv University, IL) [dblp]
- Eric Balkanski (Columbia University - New York, US) [dblp]
- Etienne Bamas (ETH Zürich, CH)
- Sanjoy Baruah (Washington University - St. Louis, US) [dblp]
- Emily Diana (TTIC - Chicago, US)
- Franziska Eberle (TU Berlin, DE) [dblp]
- Yuri Faenza (Columbia University - New York, US)
- Naveen Garg (Indian Institute of Technology - New Delhi, IN) [dblp]
- Swati Gupta (MIT - Cambridge, US)
- Sungjin Im (University of California - Santa Cruz, US) [dblp]
- Thomas Kesselheim (Universität Bonn, DE) [dblp]
- Samir Khuller (Northwestern University - Evanston, US) [dblp]
- Alexandra Lassota (TU Eindhoven, NL) [dblp]
- Alexander Lindermayr (Universität Bremen, DE)
- Alberto Marchetti-Spaccamela (Sapienza University of Rome, IT) [dblp]
- Claire Mathieu (CNRS - Paris, FR) [dblp]
- Nicole Megow (Universität Bremen, DE) [dblp]
- Benjamin J. Moseley (Carnegie Mellon University - Pittsburgh, US) [dblp]
- Viswanath Nagarajan (University of Michigan - Ann Arbor, US) [dblp]
- Seffi Naor (Technion - Haifa, IL) [dblp]
- Heather Newman (Carnegie Mellon University - Pittsburgh, US)
- Debmalya Panigrahi (Duke University - Durham, US) [dblp]
- Kirk Pruhs (University of Pittsburgh, US) [dblp]
- Lars Rohwedder (Maastricht University, NL) [dblp]
- Thomas Rothvoss (University of Washington - Seattle, US) [dblp]
- Kevin Schewior (Universität Köln, DE) [dblp]
- Ulrike Schmidt-Kraepelin (TU Eindhoven, NL) [dblp]
- Jiri Sgall (Charles University - Prague, CZ) [dblp]
- David Shmoys (Cornell University - Ithaca, US) [dblp]
- Martin Skutella (TU Berlin, DE) [dblp]
- Frits C. R. Spieksma (TU Eindhoven, NL) [dblp]
- Clifford Stein (Columbia University - New York, US) [dblp]
- Leen Stougie (CWI - Amsterdam, NL) [dblp]
- Ola Svensson (EPFL - Lausanne, CH) [dblp]
- Kavitha Telikepalli (TIFR Mumbai, IN) [dblp]
- Marc Uetz (University of Twente - Enschede, NL) [dblp]
- Adrian Vetta (McGill University - Montreal, CA)
- Tjark Vredeveld (Maastricht Univ. School of Business & Economics, NL) [dblp]
- Andreas Wiese (TU München, DE) [dblp]
- Hang Zhou (Ecole Polytechnique - Palaiseau, FR) [dblp]
- Rudy Zhou (Carnegie Mellon University - Pittsburgh, US)
Related Seminars
- Dagstuhl Seminar 08071: Scheduling (2008-02-10 - 2008-02-15) (Details)
- Dagstuhl Seminar 10071: Scheduling (2010-02-14 - 2010-02-19) (Details)
- Dagstuhl Seminar 13111: Scheduling (2013-03-10 - 2013-03-15) (Details)
- Dagstuhl Seminar 16081: Scheduling (2016-02-21 - 2016-02-26) (Details)
- Dagstuhl Seminar 18101: Scheduling (2018-03-04 - 2018-03-09) (Details)
- Dagstuhl Seminar 20081: Scheduling (2020-02-16 - 2020-02-21) (Details)
- Dagstuhl Seminar 23061: Scheduling (2023-02-05 - 2023-02-10) (Details)
Classification
- Data Structures and Algorithms
Keywords
- scheduling
- fairness
- mathematical optimization
- algorithms and complexity
- uncertainty
