Simulation Study on Quality Risk of Quality Organizational Structure Based on Agent-based Model

被引:0
|
作者
Yang Chun-hui [1 ]
Hu Tao [1 ]
Yang Lei [1 ]
Luo Zhao-hui [1 ]
机构
[1] Naval Univ Engn, Dept Management, Wuhan, Peoples R China
来源
2012 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEM) | 2012年
关键词
quality risk control; quality organizational structure; manufacturing chain;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Quality risk control is an important part of quality management. The quality risk control of manufacturing chain differs from an enterprise's quality risk control. Moreover, different quality organizational structures achieve different effects of risk control. The quality management structural model is established based on the process of quality formation and the process of information transmission in the manufacturing chain and from the approach of information distortion. On this basis, the effect of quality risk control of three different quality management structures is analyzed, while the simulation analysis on the sensitivity of relevant parameters is carried out, in order to further point out the direction and path for strengthening risk control.
引用
收藏
页码:2370 / 2374
页数:5
相关论文
共 50 条
  • [31] The agents in an agent-based economic simulation model
    Slator, BM
    Farooque, G
    INTERNATIONAL SOCIETY FOR COMPUTERS AND THEIR APPLICATIONS 11TH INTERNATIONAL CONFERENCE ON COMPUTER APPLICATIONS IN INDUSTRY AND ENGINEERING, 1998, : 175 - 178
  • [32] An Agent-Based Simulation Model for Emergency Egress
    Carrera, Alvaro
    Merino, Eduardo
    Aznar, Pablo
    Fernandez, Guillermo
    Iglesias, Carlos A.
    DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE, 2019, 801 : 140 - 148
  • [33] Agent-based model and simulation on firm size
    Shi, Zhentao
    Zeng, Jianchao
    Cui, Zhihua
    INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING, 2012, 7 (02) : 139 - 146
  • [34] An Agent-Based Simulation Model of Options Market
    Zhang Jie
    Cui Jing
    Zhai Dongsheng
    Zhang Quan
    2009 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, VOL IV, PROCEEDINGS, 2009, : 175 - 178
  • [35] Experimenting with Agent-Based Model Simulation Tools
    Antelmi, Alessia
    Cordasco, Gennaro
    D'Ambrosio, Giuseppe
    De Vinco, Daniele
    Spagnuolo, Carmine
    APPLIED SCIENCES-BASEL, 2023, 13 (01):
  • [36] Competition, risk and learning in electricity markets: An agent-based simulation study
    Aliabadi, Danial Esmaeili
    Kaya, Murat
    Sahin, Guvenc
    APPLIED ENERGY, 2017, 195 : 1000 - 1011
  • [37] Mitigating sustainability risk in supplier populations: an agent-based simulation study
    Hajmohammad, Sara
    Shevchenko, Anton
    INTERNATIONAL JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT, 2020, 40 (7-8) : 897 - 920
  • [38] Residential development simulation based on learning by agent-based model
    Mirzahossein, Hamid
    Noferesti, Vahid
    Jin, Xia
    TEMA-JOURNAL OF LAND USE MOBILITY AND ENVIRONMENT, 2022, 15 (02) : 193 - 207
  • [39] An Agent-Based Model of Flood Risk and Insurance
    Dubbelboer, Jan
    Nikolic, Igor
    Jenkins, Katie
    Hall, Jim
    JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, 2017, 20 (01):
  • [40] Agent-based simulation in the study of social dilemmas
    Gotts, NM
    Polhill, JG
    Law, ANR
    ARTIFICIAL INTELLIGENCE REVIEW, 2003, 19 (01) : 3 - 92