Research on the evolution law of cloud manufacturing service ecosystem based on multi-agent behavior simulation

Yuanfa Dong, Xiaocan Li, Wei Peng, Lei Wang, Bin Zhou
{"title":"Research on the evolution law of cloud manufacturing service ecosystem based on multi-agent behavior simulation","authors":"Yuanfa Dong, Xiaocan Li, Wei Peng, Lei Wang, Bin Zhou","doi":"10.1177/1063293x231211517","DOIUrl":null,"url":null,"abstract":"Monitoring and evaluating the health of the cloud manufacturing service ecosystem (CMSE) is critical to ensuring the long-term development of the cloud manufacturing service platform. The behavior patterns of three types of market entities, service demander (SD), service provider (SP) and platform operator (PO), have an important impact on the evolution trend of CMSE. The formulation of platform transaction rules and development of operation and regulation strategies need clarified the evolution law of the CMSE. Therefore, the evolution framework of the CMSE is constructed, the behavior modes of market entities such as SP, SD, and PO are established respectively, and multi-agent behavior simulation experiments are carried out. Simulation analysis showed that the more sensitive the SP was to the transaction activity in the system, the earlier the ecosystem in which it was located enters the stable period. When the sensitivity k of SD to the number of SPs that are available for required services in the system was between 0.45 and 0.5, the evolution trend of CMSE would show an obvious turning point from rapid growth to shrinkage. PO could adopt flexible charging strategies at different stages of the evolution of the CMSE to maximize revenue.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Concurrent Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/1063293x231211517","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Monitoring and evaluating the health of the cloud manufacturing service ecosystem (CMSE) is critical to ensuring the long-term development of the cloud manufacturing service platform. The behavior patterns of three types of market entities, service demander (SD), service provider (SP) and platform operator (PO), have an important impact on the evolution trend of CMSE. The formulation of platform transaction rules and development of operation and regulation strategies need clarified the evolution law of the CMSE. Therefore, the evolution framework of the CMSE is constructed, the behavior modes of market entities such as SP, SD, and PO are established respectively, and multi-agent behavior simulation experiments are carried out. Simulation analysis showed that the more sensitive the SP was to the transaction activity in the system, the earlier the ecosystem in which it was located enters the stable period. When the sensitivity k of SD to the number of SPs that are available for required services in the system was between 0.45 and 0.5, the evolution trend of CMSE would show an obvious turning point from rapid growth to shrinkage. PO could adopt flexible charging strategies at different stages of the evolution of the CMSE to maximize revenue.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于多智能体行为仿真的云制造服务生态系统演化规律研究
监测和评估云制造服务生态系统(CMSE)的健康状况,对于确保云制造服务平台的长期发展至关重要。服务需求者(SD)、服务提供者(SP)和平台运营者(PO)这三种市场主体的行为模式对CMSE的演化趋势有重要影响。平台交易规则的制定和运营监管策略的制定都需要明确CMSE的演进规律。为此,构建了CMSE的演化框架,分别建立了SP、SD、PO等市场主体的行为模式,并进行了多智能体行为仿真实验。仿真分析表明,SP对系统中的交易活动越敏感,其所在生态系统进入稳定期的时间越早。当SD对系统中可用于所需业务的sp数的灵敏度k在0.45 ~ 0.5之间时,CMSE的演化趋势将出现由快速增长到收缩的明显拐点。PO可以在CMSE发展的不同阶段采取灵活的收费策略,以实现收益最大化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Sensitivity study of process parameters of wire arc additive manufacturing using probabilistic deep learning and uncertainty quantification Retraction Notice Decision-making solutions based artificial intelligence and hybrid software for optimal sizing and energy management in a smart grid system Harness collaboration between manufacturing Small and medium-sized enterprises through a collaborative platform based on the business model canvas Research on the evolution law of cloud manufacturing service ecosystem based on multi-agent behavior simulation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1