IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v44y2022i5d10.1007_s10878-022-00916-4.html
   My bibliography  Save this article

Learn from history for online bipartite matching

Author

Listed:
  • Huili Zhang

    (Xi’an Jiaotong University)

  • Rui Du

    (Xi’an Jiaotong University)

  • Kelin Luo

    (University of Bonn)

  • Weitian Tong

    (Georgia Southern University)

Abstract

Motivated by various applications in the online platforms for ride-hailing and crowd-sourcing delivery, we study the edge-weighted online bipartite matching (EWOBM) problem. We assume a part of online vertices are released in advance to mimic historical information that the algorithm is able to access. Different from traditional approaches that usually learn informative distributions from large enough history sets, our algorithms enable to extra useful information for the history set of any size. When the online vertices arrive in a random order, we present an online algorithm, named as h -TP-OM, achieving a competitive ratio that increases as more historical information is considered. However, once enough historical information has been fed to the algorithm, additional historical information becomes useless. Based on h -TP-OM, we then propose a time-efficient greedy heuristic, named as h -TP-G, which even has better performances in applications, particularly on large-scale instances. When the arrival order of online vertices is determined by an adversary, we present another greedy heuristic algorithm, named as Greedy-RT. Experiments on both synthetic and real-world datasets are conducted to evaluate the practical performances of the proposed algorithms. The experiment results demonstrate the usefulness of historical information for both h -TP-OM and h -TP-G, and also show the time efficiency of h -TP-G and Greedy-RT.

Suggested Citation

  • Huili Zhang & Rui Du & Kelin Luo & Weitian Tong, 2022. "Learn from history for online bipartite matching," Journal of Combinatorial Optimization, Springer, vol. 44(5), pages 3611-3640, December.
    • Handle: RePEc:spr:jcomop:v:44:y:2022:i:5:d:10.1007_s10878-022-00916-4
    DOI: 10.1007/s10878-022-00916-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-022-00916-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10878-022-00916-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Patrick Jaillet & Xin Lu, 2014. "Online Stochastic Matching: New Algorithms with Better Bounds," Mathematics of Operations Research, INFORMS, vol. 39(3), pages 624-646, August.
    2. H. W. Kuhn, 1955. "The Hungarian method for the assignment problem," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 2(1‐2), pages 83-97, March.
    3. Vahideh H. Manshadi & Shayan Oveis Gharan & Amin Saberi, 2012. "Online Stochastic Matching: Online Actions Based on Offline Statistics," Mathematics of Operations Research, INFORMS, vol. 37(4), pages 559-573, November.
    4. Xiaoming Sun & Jia Zhang & Jialin Zhang, 2017. "Near optimal algorithms for online weighted bipartite matching in adversary model," Journal of Combinatorial Optimization, Springer, vol. 34(3), pages 689-705, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Singer, Gerald & Köll, Rebekka & Aichhorn, Lukas & Pertl, Patrick & Trattner, Alexander, 2023. "Utilizing hydrogen pressure energy by expansion machines – PEM fuel cells in mobile and other potential applications," Applied Energy, Elsevier, vol. 343(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hao Wang & Zhenzhen Yan & Xiaohui Bei, 2022. "A nonasymptotic analysis for re‐solving heuristic in online matching," Production and Operations Management, Production and Operations Management Society, vol. 31(8), pages 3096-3124, August.
    2. Vahideh Manshadi & Scott Rodilitz, 2022. "Online Policies for Efficient Volunteer Crowdsourcing," Management Science, INFORMS, vol. 68(9), pages 6572-6590, September.
    3. András Frank, 2005. "On Kuhn's Hungarian Method—A tribute from Hungary," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(1), pages 2-5, February.
    4. Amit Kumar & Anila Gupta, 2013. "Mehar’s methods for fuzzy assignment problems with restrictions," Fuzzy Information and Engineering, Springer, vol. 5(1), pages 27-44, March.
    5. Parvin Ahmadi & Iman Gholampour & Mahmoud Tabandeh, 2018. "Cluster-based sparse topical coding for topic mining and document clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 537-558, September.
    6. Patrick Jaillet & Xin Lu, 2014. "Online Stochastic Matching: New Algorithms with Better Bounds," Mathematics of Operations Research, INFORMS, vol. 39(3), pages 624-646, August.
    7. Chenchen Ma & Jing Ouyang & Gongjun Xu, 2023. "Learning Latent and Hierarchical Structures in Cognitive Diagnosis Models," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 175-207, March.
    8. Alberto Vera & Siddhartha Banerjee, 2021. "The Bayesian Prophet: A Low-Regret Framework for Online Decision Making," Management Science, INFORMS, vol. 67(3), pages 1368-1391, March.
    9. Tran Hoang Hai, 2020. "Estimation of volatility causality in structural autoregressions with heteroskedasticity using independent component analysis," Statistical Papers, Springer, vol. 61(1), pages 1-16, February.
    10. Johannes Baumler & Martin Bullinger & Stefan Kober & Donghao Zhu, 2022. "Superiority of Instantaneous Decisions in Thin Dynamic Matching Markets," Papers 2206.10287, arXiv.org, revised Jun 2023.
    11. Caplin, Andrew & Leahy, John, 2020. "Comparative statics in markets for indivisible goods," Journal of Mathematical Economics, Elsevier, vol. 90(C), pages 80-94.
    12. Biró, Péter & Gudmundsson, Jens, 2021. "Complexity of finding Pareto-efficient allocations of highest welfare," European Journal of Operational Research, Elsevier, vol. 291(2), pages 614-628.
    13. Péter Biró & Flip Klijn & Xenia Klimentova & Ana Viana, 2021. "Shapley-Scarf Housing Markets: Respecting Improvement, Integer Programming, and Kidney Exchange," Working Papers 1235, Barcelona School of Economics.
    14. Michal Brylinski, 2014. "eMatchSite: Sequence Order-Independent Structure Alignments of Ligand Binding Pockets in Protein Models," PLOS Computational Biology, Public Library of Science, vol. 10(9), pages 1-15, September.
    15. Chiwei Yan & Helin Zhu & Nikita Korolko & Dawn Woodard, 2020. "Dynamic pricing and matching in ride‐hailing platforms," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(8), pages 705-724, December.
    16. Fanrong Xie & Anuj Sharma & Zuoan Li, 2022. "An alternate approach to solve two-level priority based assignment problem," Computational Optimization and Applications, Springer, vol. 81(2), pages 613-656, March.
    17. Yan, Pengyu & Lee, Chung-Yee & Chu, Chengbin & Chen, Cynthia & Luo, Zhiqin, 2021. "Matching and pricing in ride-sharing: Optimality, stability, and financial sustainability," Omega, Elsevier, vol. 102(C).
    18. Guo, Yuhan & Zhang, Yu & Boulaksil, Youssef, 2021. "Real-time ride-sharing framework with dynamic timeframe and anticipation-based migration," European Journal of Operational Research, Elsevier, vol. 288(3), pages 810-828.
    19. Demetrescu, Camil & Lupia, Francesco & Mendicelli, Angelo & Ribichini, Andrea & Scarcello, Francesco & Schaerf, Marco, 2019. "On the Shapley value and its application to the Italian VQR research assessment exercise," Journal of Informetrics, Elsevier, vol. 13(1), pages 87-104.
    20. Christian Billing & Florian Jaehn & Thomas Wensing, 2020. "Fair task allocation problem," Annals of Operations Research, Springer, vol. 284(1), pages 131-146, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcomop:v:44:y:2022:i:5:d:10.1007_s10878-022-00916-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.