IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0282312.html
   My bibliography  Save this article

Dynamic three-sided matching model for personnel–robot-position matching problem in intelligent environments

Author

Listed:
  • Zhi-chao Liang
  • Yu Yang
  • Qiu Xie
  • Jing Wang
  • Xue-jiao Zhang
  • Bao-dong Li

Abstract

In recent years, intelligent robots have facilitated intelligent production, and a new type of problem (personnel–robot-position matching (PRPM)) has been encountered in personnel–position matching (PPM). In this study, a dynamic three-sided matching model is proposed to solve the PRPM problem in an intelligent production line based on man–machine collaboration. The first issue considered is setting the dynamic reference point, which is addressed in the information evaluation phase by proposing a method for setting the dynamic reference point based on the prospect theory. Another important issue involves multistage preference information integration, wherein a probability density function and a value function are introduced. Considering the attenuation of preference information in a time series, the attenuation index model is introduced to calculate the satisfaction matrix. Furthermore, a dynamic three-sided matching model is established. Additionally, a multi-objective decision-making model is established to optimize the matching of multiple sides (personnel, intelligent robots, and positions). Subsequently, the model is transformed into a single objective model using the triangular balance principle, which is introduced to obtain the final optimisation results in this modelling process. A case study is presented to illustrate the practicality of the dynamic three-sided matching model in intelligent environments. The results indicate that this model can solve the PRPM problem in an intelligent production line.

Suggested Citation

  • Zhi-chao Liang & Yu Yang & Qiu Xie & Jing Wang & Xue-jiao Zhang & Bao-dong Li, 2023. "Dynamic three-sided matching model for personnel–robot-position matching problem in intelligent environments," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-22, April.
  • Handle: RePEc:plo:pone00:0282312
    DOI: 10.1371/journal.pone.0282312
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0282312
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0282312&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0282312?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
    ---><---

    References listed on IDEAS

    as
    1. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    2. Wenyu Yu & Zhen Zhang & Qiuyan Zhong, 2021. "Consensus reaching for MAGDM with multi-granular hesitant fuzzy linguistic term sets: a minimum adjustment-based approach," Annals of Operations Research, Springer, vol. 300(2), pages 443-466, May.
    3. Liwei Zhong & Yanqin Bai, 2019. "Three-sided stable matching problem with two of them as cooperative partners," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 286-292, January.
    Full references (including those not matched with items on IDEAS)

    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. Oliver Linton & Esfandiar Maasoumi & Yoon-Jae Wang, 2002. "Consistent testing for stochastic dominance: a subsampling approach," CeMMAP working papers 03/02, Institute for Fiscal Studies.
    2. Heiko Karle & Georg Kirchsteiger & Martin Peitz, 2015. "Loss Aversion and Consumption Choice: Theory and Experimental Evidence," American Economic Journal: Microeconomics, American Economic Association, vol. 7(2), pages 101-120, May.
    3. Shoji, Isao & Kanehiro, Sumei, 2016. "Disposition effect as a behavioral trading activity elicited by investors' different risk preferences," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 104-112.
    4. Muhammad Kashif & Thomas Leirvik, 2022. "The MAX Effect in an Oil Exporting Country: The Case of Norway," JRFM, MDPI, vol. 15(4), pages 1-16, March.
    5. Boone, Jan & Sadrieh, Abdolkarim & van Ours, Jan C., 2009. "Experiments on unemployment benefit sanctions and job search behavior," European Economic Review, Elsevier, vol. 53(8), pages 937-951, November.
    6. Jos'e Cl'audio do Nascimento, 2019. "Behavioral Biases and Nonadditive Dynamics in Risk Taking: An Experimental Investigation," Papers 1908.01709, arXiv.org, revised Apr 2023.
    7. Martín Egozcue & Sébastien Massoni & Wing-Keung Wong & RiÄ ardas Zitikis, 2012. "Integration-segregation decisions under general value functions: "Create your own bundle — choose 1, 2, or all 3!"," Documents de travail du Centre d'Economie de la Sorbonne 12057, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    8. Howard Kunreuther & Erwann Michel-Kerjan, 2015. "Demand for fixed-price multi-year contracts: Experimental evidence from insurance decisions," Journal of Risk and Uncertainty, Springer, vol. 51(2), pages 171-194, October.
    9. Francesco GUALA, 2017. "Preferences: Neither Behavioural nor Mental," Departmental Working Papers 2017-05, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    10. Shi, Yun & Cui, Xiangyu & Zhou, Xunyu, 2020. "Beta and Coskewness Pricing: Perspective from Probability Weighting," SocArXiv 5rqhv, Center for Open Science.
    11. Bin Zou, 2017. "Optimal Investment In Hedge Funds Under Loss Aversion," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(03), pages 1-32, May.
    12. Alex Stomper & Marie-Louise Vierø, 2015. "Iterated Expectations Under Rank-dependent Expected Utility And Model Consistency," Working Paper 1228, Economics Department, Queen's University.
    13. Liu, Zhiqiang & Yan, Miao & Fan, Youqing & Chen, Liling, 2021. "Ascribed or achieved? The role of birth order on innovative behaviour in the workplace," Journal of Business Research, Elsevier, vol. 134(C), pages 480-492.
    14. Kazi Iqbal & Asad Islam & John List & Vy Nguyen, 2021. "Myopic Loss Aversion and Investment Decisions: From the Laboratory to the Field," Framed Field Experiments 000730, The Field Experiments Website.
    15. Filiz-Ozbay, Emel & Guryan, Jonathan & Hyndman, Kyle & Kearney, Melissa & Ozbay, Erkut Y., 2015. "Do lottery payments induce savings behavior? Evidence from the lab," Journal of Public Economics, Elsevier, vol. 126(C), pages 1-24.
    16. Shuli Liu & Xinwang Liu, 2016. "A Sample Survey Based Linguistic MADM Method with Prospect Theory for Online Shopping Problems," Group Decision and Negotiation, Springer, vol. 25(4), pages 749-774, July.
    17. Nicholas Barberis, 2012. "A Model of Casino Gambling," Management Science, INFORMS, vol. 58(1), pages 35-51, January.
    18. Goytom Abraha Kahsay & Daniel Osberghaus, 2018. "Storm Damage and Risk Preferences: Panel Evidence from Germany," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(1), pages 301-318, September.
    19. Carolin Bock & Maximilian Schmidt, 2015. "Should I stay, or should I go? – How fund dynamics influence venture capital exit decisions," Review of Financial Economics, John Wiley & Sons, vol. 27(1), pages 68-82, November.
    20. Kai Barron & Luis F. Gamboa & Paul Rodriguez-Lesmes, 2016. "Behavioural Response to a Sudden Health Risk: Dengue and Educational Outcomes in Colombia," Documentos de trabajo 17667, Escuela de Gobierno - Universidad de los Andes.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0282312. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.