IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v232y2014i1p137-145.html
   My bibliography  Save this article

Allocation of information granularity in optimization and decision-making models: Towards building the foundations of Granular Computing

Author

Listed:
  • Pedrycz, Witold

Abstract

The highly diversified conceptual and algorithmic landscape of Granular Computing calls for the formation of sound fundamentals of the discipline, which cut across the diversity of formal frameworks (fuzzy sets, sets, rough sets) in which information granules are formed and processed. The study addresses this quest by introducing an idea of granular models – generalizations of numeric models that are formed as a result of an optimal allocation (distribution) of information granularity. Information granularity is regarded as a crucial design asset, which helps establish a better rapport of the resulting granular model with the system under modeling. A suite of modeling situations is elaborated on; they offer convincing examples behind the emergence of granular models. Pertinent problems showing how information granularity is distributed throughout the parameters of numeric functions (and resulting in granular mappings) are formulated as optimization tasks. A set of associated information granularity distribution protocols is discussed. We also provide a number of illustrative examples.

Suggested Citation

  • Pedrycz, Witold, 2014. "Allocation of information granularity in optimization and decision-making models: Towards building the foundations of Granular Computing," European Journal of Operational Research, Elsevier, vol. 232(1), pages 137-145.
    • Handle: RePEc:eee:ejores:v:232:y:2014:i:1:p:137-145
    DOI: 10.1016/j.ejor.2012.03.038
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221712002482
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2012.03.038?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. Dembczynski, Krzysztof & Greco, Salvatore & Slowinski, Roman, 2009. "Rough set approach to multiple criteria classification with imprecise evaluations and assignments," European Journal of Operational Research, Elsevier, vol. 198(2), pages 626-636, October.
    2. Herrera, F. & Martinez, L. & Sanchez, P. J., 2005. "Managing non-homogeneous information in group decision making," European Journal of Operational Research, Elsevier, vol. 166(1), pages 115-132, October.
    3. Chiclana, F. & Herrera-Viedma, E. & Herrera, F. & Alonso, S., 2007. "Some induced ordered weighted averaging operators and their use for solving group decision-making problems based on fuzzy preference relations," European Journal of Operational Research, Elsevier, vol. 182(1), pages 383-399, October.
    4. Lingras, P. & Butz, C.J., 2010. "Rough support vector regression," European Journal of Operational Research, Elsevier, vol. 206(2), pages 445-455, 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. Dan Wang & Yukang Liu & Zhenhua Yu, 2023. "Synergistic Mechanism of Designing Information Granules with the Use of the Principle of Justifiable Granularity," Mathematics, MDPI, vol. 11(7), pages 1-19, April.
    2. Der-Chiang Li & Wu-Kuo Lin & Liang-Sian Lin & Chien-Chih Chen & Wen-Ting Huang, 2017. "The attribute-trend-similarity method to improve learning performance for small datasets," International Journal of Production Research, Taylor & Francis Journals, vol. 55(7), pages 1898-1913, April.
    3. Dias, Sónia & Brito, Paula, 2017. "Off the beaten track: A new linear model for interval data," European Journal of Operational Research, Elsevier, vol. 258(3), pages 1118-1130.
    4. Ouyang, Yao & Pedrycz, Witold, 2016. "A new model for intuitionistic fuzzy multi-attributes decision making," European Journal of Operational Research, Elsevier, vol. 249(2), pages 677-682.
    5. Zhaofeng Zhong & Ge Zhang & Li Yin & Yufeng Chen, 2023. "Description and Analysis of Data Security Based on Differential Privacy in Enterprise Power Systems," Mathematics, MDPI, vol. 11(23), pages 1-20, November.

    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. Dong, Yucheng & Xu, Yinfeng & Li, Hongyi & Feng, Bo, 2010. "The OWA-based consensus operator under linguistic representation models using position indexes," European Journal of Operational Research, Elsevier, vol. 203(2), pages 455-463, June.
    2. Eduardo Fernandez & Jorge Navarro & Rafael Olmedo, 2018. "Characterization of the Effectiveness of Several Outranking-Based Multi-Criteria Sorting Methods," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(04), pages 1047-1084, July.
    3. Liu, Jiapeng & Liao, Xiuwu & Yang, Jian-bo, 2015. "A group decision-making approach based on evidential reasoning for multiple criteria sorting problem with uncertainty," European Journal of Operational Research, Elsevier, vol. 246(3), pages 858-873.
    4. Junling Zhang & Xiaowen Qi & Changyong Liang, 2018. "Tackling Complexity in Green Contractor Selection for Mega Infrastructure Projects: A Hesitant Fuzzy Linguistic MADM Approach with considering Group Attitudinal Character and Attributes’ Interdependen," Complexity, Hindawi, vol. 2018, pages 1-31, December.
    5. Russ McBride & Mark D. Packard & Brent B. Clark, 2024. "Rogue Entrepreneurship," Entrepreneurship Theory and Practice, , vol. 48(1), pages 392-417, January.
    6. Zhou-Jing Wang & Yuhong Wang & Kevin W. Li, 2016. "An Acceptable Consistency-Based Framework for Group Decision Making with Intuitionistic Preference Relations," Group Decision and Negotiation, Springer, vol. 25(1), pages 181-202, January.
    7. Bowen Zhang & Yucheng Dong & Enrique Herrera-Viedma, 2019. "Group Decision Making with Heterogeneous Preference Structures: An Automatic Mechanism to Support Consensus Reaching," Group Decision and Negotiation, Springer, vol. 28(3), pages 585-617, June.
    8. Al-Ebbini, Lina & Oztekin, Asil & Chen, Yao, 2016. "FLAS: Fuzzy lung allocation system for US-based transplantations," European Journal of Operational Research, Elsevier, vol. 248(3), pages 1051-1065.
    9. Jian Wu, 2016. "Consistency in MCGDM Problems with Intuitionistic Fuzzy Preference Relations Based on an Exponential Score Function," Group Decision and Negotiation, Springer, vol. 25(2), pages 399-420, March.
    10. Xiqin Wen & Mingchao Yan & Jieyu Xian & Rui Yue & Anhua Peng, 2016. "Supplier selection in supplier chain management using Choquet integral-based linguistic operators under fuzzy heterogeneous environment," Fuzzy Optimization and Decision Making, Springer, vol. 15(3), pages 307-330, September.
    11. Cheng, Yujie & Song, Dengwei & Wang, Zhenya & Lu, Chen & Zerhouni, Noureddine, 2020. "An ensemble prognostic method for lithium-ion battery capacity estimation based on time-varying weight allocation," Applied Energy, Elsevier, vol. 266(C).
    12. Gao, Jianwei & Li, Ming & Liu, Huihui, 2015. "Generalized ordered weighted utility averaging-hyperbolic absolute risk aversion operators and their applications to group decision-making," European Journal of Operational Research, Elsevier, vol. 243(1), pages 258-270.
    13. Raquel González del Pozo & Luis C. Dias & José Luis García-Lapresta, 2020. "Using Different Qualitative Scales in a Multi-Criteria Decision-Making Procedure," Mathematics, MDPI, vol. 8(3), pages 1-20, March.
    14. Marcelo Loor & Ana Tapia-Rosero & Guy De Tré, 2021. "Towards Better Concordance among Contextualized Evaluations in FAST-GDM Problems," Mathematics, MDPI, vol. 9(1), pages 1-17, January.
    15. Pingtao Yi & Qiankun Dong & Weiwei Li, 2021. "A family of IOWA operators with reliability measurement under interval-valued group decision-making environment," Group Decision and Negotiation, Springer, vol. 30(3), pages 483-505, June.
    16. Zhang, Huanhuan & Kou, Gang & Peng, Yi, 2019. "Soft consensus cost models for group decision making and economic interpretations," European Journal of Operational Research, Elsevier, vol. 277(3), pages 964-980.
    17. Fernández, Eduardo & Figueira, José Rui & Navarro, Jorge & Solares, Efrain, 2023. "A generalized approach to ordinal classification based on the comparison of actions with either limiting or characteristic profiles," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1309-1322.
    18. Zhang, Bowen & Dong, Yucheng & Zhang, Hengjie & Pedrycz, Witold, 2020. "Consensus mechanism with maximum-return modifications and minimum-cost feedback: A perspective of game theory," European Journal of Operational Research, Elsevier, vol. 287(2), pages 546-559.
    19. Mi, Yunlong & Wang, Zongrun & Liu, Hui & Qu, Yi & Yu, Gaofeng & Shi, Yong, 2023. "Divide and conquer: A granular concept-cognitive computing system for dynamic classification decision making," European Journal of Operational Research, Elsevier, vol. 308(1), pages 255-273.
    20. Fernandez, Eduardo & Olmedo, Rafael, 2013. "An outranking-based general approach to solving group multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 225(3), pages 497-506.

    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:eee:ejores:v:232:y:2014:i:1:p:137-145. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.