A Data-Driven Method for Supporting Self-Adapt Large-Scale Group Decision-Making: A Case Study on Resilient Design of Firm’s Product
Large-scale group decision-making (LSGDM) has emerged as a prominent research area in various domains, such as high technology and complex engineering problems. The advent of machine learning techniques has revolutionized LSGDM by introducing new data-driven approaches. First, recurrent neural netwo...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2024/2328960 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832559258939424768 |
|---|---|
| author | Houxue Xia Mingwei Liu Jingyao Jiao Huagang Tong Haifeng Zhang |
| author_facet | Houxue Xia Mingwei Liu Jingyao Jiao Huagang Tong Haifeng Zhang |
| author_sort | Houxue Xia |
| collection | DOAJ |
| description | Large-scale group decision-making (LSGDM) has emerged as a prominent research area in various domains, such as high technology and complex engineering problems. The advent of machine learning techniques has revolutionized LSGDM by introducing new data-driven approaches. First, recurrent neural networks (RNNs) have been proposed as a data-driven method to effectively learn and predict experts’ preferences. Second, a self-adaptive method has been devised to optimize clustering parameters, considering their influence. The consensus-reaching process facilitates the reverse optimization of these parameters. Third, a novel approach called analysis target cascading (ATC) has been suggested to address the limitations of traditional weighing methods used in previous LSGDM studies. ATC comprehensively investigates the potential game among multiple subgroups, thereby resolving the consensus-reaching problem (CRP). Lastly, an improved artificial bee colony algorithm has been proposed to tackle the optimization problem presented in this study. This enhanced algorithm incorporates the levying mechanism and searching method from the gravity search algorithm. To validate the efficacy of the proposed methods, a case study involving a large-scale interdisciplinary team has been conducted. |
| format | Article |
| id | doaj-art-1012880e2e7f4f5bbab6ac6c695a168e |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-1012880e2e7f4f5bbab6ac6c695a168e2025-02-03T01:30:21ZengWileyJournal of Mathematics2314-47852024-01-01202410.1155/2024/2328960A Data-Driven Method for Supporting Self-Adapt Large-Scale Group Decision-Making: A Case Study on Resilient Design of Firm’s ProductHouxue Xia0Mingwei Liu1Jingyao Jiao2Huagang Tong3Haifeng Zhang4School of Management and EconomicsSchool of Management and EconomicsSchool of Management and EconomicsSchool of Management and EconomicsSchool of Health AdministrationLarge-scale group decision-making (LSGDM) has emerged as a prominent research area in various domains, such as high technology and complex engineering problems. The advent of machine learning techniques has revolutionized LSGDM by introducing new data-driven approaches. First, recurrent neural networks (RNNs) have been proposed as a data-driven method to effectively learn and predict experts’ preferences. Second, a self-adaptive method has been devised to optimize clustering parameters, considering their influence. The consensus-reaching process facilitates the reverse optimization of these parameters. Third, a novel approach called analysis target cascading (ATC) has been suggested to address the limitations of traditional weighing methods used in previous LSGDM studies. ATC comprehensively investigates the potential game among multiple subgroups, thereby resolving the consensus-reaching problem (CRP). Lastly, an improved artificial bee colony algorithm has been proposed to tackle the optimization problem presented in this study. This enhanced algorithm incorporates the levying mechanism and searching method from the gravity search algorithm. To validate the efficacy of the proposed methods, a case study involving a large-scale interdisciplinary team has been conducted.http://dx.doi.org/10.1155/2024/2328960 |
| spellingShingle | Houxue Xia Mingwei Liu Jingyao Jiao Huagang Tong Haifeng Zhang A Data-Driven Method for Supporting Self-Adapt Large-Scale Group Decision-Making: A Case Study on Resilient Design of Firm’s Product Journal of Mathematics |
| title | A Data-Driven Method for Supporting Self-Adapt Large-Scale Group Decision-Making: A Case Study on Resilient Design of Firm’s Product |
| title_full | A Data-Driven Method for Supporting Self-Adapt Large-Scale Group Decision-Making: A Case Study on Resilient Design of Firm’s Product |
| title_fullStr | A Data-Driven Method for Supporting Self-Adapt Large-Scale Group Decision-Making: A Case Study on Resilient Design of Firm’s Product |
| title_full_unstemmed | A Data-Driven Method for Supporting Self-Adapt Large-Scale Group Decision-Making: A Case Study on Resilient Design of Firm’s Product |
| title_short | A Data-Driven Method for Supporting Self-Adapt Large-Scale Group Decision-Making: A Case Study on Resilient Design of Firm’s Product |
| title_sort | data driven method for supporting self adapt large scale group decision making a case study on resilient design of firm s product |
| url | http://dx.doi.org/10.1155/2024/2328960 |
| work_keys_str_mv | AT houxuexia adatadrivenmethodforsupportingselfadaptlargescalegroupdecisionmakingacasestudyonresilientdesignoffirmsproduct AT mingweiliu adatadrivenmethodforsupportingselfadaptlargescalegroupdecisionmakingacasestudyonresilientdesignoffirmsproduct AT jingyaojiao adatadrivenmethodforsupportingselfadaptlargescalegroupdecisionmakingacasestudyonresilientdesignoffirmsproduct AT huagangtong adatadrivenmethodforsupportingselfadaptlargescalegroupdecisionmakingacasestudyonresilientdesignoffirmsproduct AT haifengzhang adatadrivenmethodforsupportingselfadaptlargescalegroupdecisionmakingacasestudyonresilientdesignoffirmsproduct AT houxuexia datadrivenmethodforsupportingselfadaptlargescalegroupdecisionmakingacasestudyonresilientdesignoffirmsproduct AT mingweiliu datadrivenmethodforsupportingselfadaptlargescalegroupdecisionmakingacasestudyonresilientdesignoffirmsproduct AT jingyaojiao datadrivenmethodforsupportingselfadaptlargescalegroupdecisionmakingacasestudyonresilientdesignoffirmsproduct AT huagangtong datadrivenmethodforsupportingselfadaptlargescalegroupdecisionmakingacasestudyonresilientdesignoffirmsproduct AT haifengzhang datadrivenmethodforsupportingselfadaptlargescalegroupdecisionmakingacasestudyonresilientdesignoffirmsproduct |