OASIcs.ATMOS.2023.17.pdf
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Optimizing Fairness over Time with Homogeneous Workers (Short Paper)
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23rd Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2023)
Series: Open Access Series in Informatics (OASIcs)
Conference: Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2023-08-31
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Bart van Rossum, Rui Chen, and Andrea Lodi. Optimizing Fairness over Time with Homogeneous Workers (Short Paper). In 23rd Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2023). Open Access Series in Informatics (OASIcs), Volume 115, pp. 17:1-17:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/OASIcs.ATMOS.2023.17
https://doi.org/10.4230/OASIcs.ATMOS.2023.17
Abstract
There is growing interest in including fairness in optimization models. In particular, the concept of fairness over time, or, long-term fairness, is gaining attention. In this paper, we focus on fairness over time in online optimization problems involving the assignment of work to multiple homogeneous workers. This encompasses many real-life problems, including variants of the vehicle routing problem and the crew scheduling problem. The online assignment problem with fairness over time is formally defined. We propose a simple and interpretable assignment policy with some desirable properties. In addition, we perform a case study on the capacitated vehicle routing problem. Empirically, we show that the most cost-efficient solution usually results in unfair assignments while much more fair solutions can be attained with minor efficiency loss using our policy.
Subject Classification
ACM Subject Classification
- Mathematics of computing → Combinatorial optimization
Keywords
- Fairness
- Online Optimization
- Combinatorial Optimization
- Vehicle Routing
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References
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