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Hiring Secretaries over Time: The Benefit of Concurrent Employment

Author

Listed:
  • Yann Disser

    (Graduate School of Computational Engineering, Technical University of Darmstadt, Darmstadt 64293, Germany;)

  • John Fearnley

    (Department of Computer Science, University of Liverpool, Liverpool L69 3BX, United Kingdom;)

  • Martin Gairing

    (Department of Computer Science, University of Liverpool, Liverpool L69 3BX, United Kingdom;)

  • Oliver Göbel

    (Department of Computer Science, RWTH Aachen University, Aachen 52062, Germany;)

  • Max Klimm

    (School of Business and Economics, Humboldt University of Berlin, Berlin 10099, Germany;)

  • Daniel Schmand

    (Department of Computer Science, Goethe University Frankfurt, Frankfurt am Main 60054, Germany;)

  • Alexander Skopalik

    (Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede 7500 AE, Netherlands;)

  • Andreas Tönnis

    (Institute of Computer Science, University of Bonn, Bonn 53115, Germany)

Abstract

We consider a stochastic online problem where n applicants arrive over time, one per time step. Upon the arrival of each applicant, their cost per time step is revealed, and we have to fix the duration of employment, starting immediately. This decision is irrevocable; that is, we can neither extend a contract nor dismiss a candidate once hired. In every time step, at least one candidate needs to be under contract, and our goal is to minimize the total hiring cost, which is the sum of the applicants’ costs multiplied with their respective employment durations. We provide a competitive online algorithm for the case that the applicants’ costs are drawn independently from a known distribution. Specifically, the algorithm achieves a competitive ratio of 2.965 for the case of uniform distributions. For this case, we give an analytical lower bound of 2 and a computational lower bound of 2.148. We then adapt our algorithm to stay competitive even in settings with one or more of the following restrictions: (i) at most two applicants can be hired concurrently; (ii) the distribution of the applicants’ costs is unknown; (iii) the total number n of time steps is unknown. On the other hand, we show that concurrent employment is a necessary feature of competitive algorithms by proving that no algorithm has a competitive ratio better than Ω ( n / l o g n ) if concurrent employment is forbidden.

Suggested Citation

  • Yann Disser & John Fearnley & Martin Gairing & Oliver Göbel & Max Klimm & Daniel Schmand & Alexander Skopalik & Andreas Tönnis, 2020. "Hiring Secretaries over Time: The Benefit of Concurrent Employment," Mathematics of Operations Research, INFORMS, vol. 45(1), pages 323-352, February.
  • Handle: RePEc:inm:ormoor:v:45:y:2020:i:1:p:323-352
    DOI: 10.1287/moor.2019.0993
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    References listed on IDEAS

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    1. Richard Bellman, 1954. "Some Applications of the Theory of Dynamic Programming---A Review," Operations Research, INFORMS, vol. 2(3), pages 275-288, August.
    2. Roger B. Myerson, 1981. "Optimal Auction Design," Mathematics of Operations Research, INFORMS, vol. 6(1), pages 58-73, February.
    3. Shuchi Chawla & Jason Hartline & David Malec & Balasubramanian Sivan, 2010. "Sequential Posted Pricing and Multi-parameter Mechanism Design," Discussion Papers 1486, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    4. Richard Bellman, 1954. "On some applications of the theory of dynamic programming to logistics," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 1(2), pages 141-153, June.
    5. Kennedy, D. P., 1987. "Prophet-type inequalities for multi-choice optimal stopping," Stochastic Processes and their Applications, Elsevier, vol. 24(1), pages 77-88, February.
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    Cited by:

    1. Sebastian Perez-Salazar & Victor Verdugo, 2024. "Optimal Guarantees for Online Selection Over Time," Papers 2408.11224, arXiv.org.

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