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On fairness as a rostering objective

Published: 13 July 2019 Publication History

Abstract

Many combinatorial optimization problems involve scheduling or ordering work that will ultimately be completed by a company's employees. If solution quality is measured by a simple weighted sum of the constraint violations for each employee, an optimizer may produce solutions in which a small number of employees suffer a highly disproportionate share of these violations. We present the results of experiments in generating rosters whilst considering fairness as an additional optimization objective.

References

[1]
Piotr Czyzżak and Adrezej Jaszkiewicz. 1998. Pareto simulated annealing --- a metaheuristic technique for multiple-objective combinatorial optimization. Journal of Multi-Criteria Decision Analysis 7, 1 (1998), 34--47.
[2]
Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 2 (2002), 182--197.
[3]
Anna Lavygina, Kris Welsh, and Alan Crispin. 2017. Doctor rostering in compliance with the new UK junior doctor contract. In International Conference on Combinatorial Optimization and Applications. Springer, 394--408.
[4]
Djamila Ouelhadj, Simon Martin, Pieter Smet, Ender Ozean, and G Vanden Berghe. 2012. Fairness in nurse rostering. (2012).
[5]
Paolo Serafini. 1994. Simulated annealing for multi objective optimization problems. In Multiple Criteria Decision Making. Springer, 283--292.
[6]
Jorne Van den Bergh, Jeroen Beliën, Philippe De Bruecker, Erik Demeulemeester, and Liesje De Boeck. 2013. Personnel scheduling: A literature review. European Journal of Operational Research 226, 3 (2013), 367--385.

Cited By

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  • (2025)Optimization Techniques for Physician Scheduling Problem: A Systematic Review of Recent Advancements and Future DirectionsIEEE Access10.1109/ACCESS.2024.352459913(5203-5218)Online publication date: 2025

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cover image ACM Conferences
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2019
2161 pages
ISBN:9781450367486
DOI:10.1145/3319619
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2019

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Author Tags

  1. constrained optimisation
  2. evolutionary computing
  3. multiobjective optimisation
  4. simulated annealing
  5. staff rostering

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GECCO '19
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GECCO '19: Genetic and Evolutionary Computation Conference
July 13 - 17, 2019
Prague, Czech Republic

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2025)Optimization Techniques for Physician Scheduling Problem: A Systematic Review of Recent Advancements and Future DirectionsIEEE Access10.1109/ACCESS.2024.352459913(5203-5218)Online publication date: 2025

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