Group Multirole Assignment With Cooperation, Conflict, and Public Interest Factors

被引:2
作者
Ke, Xintong [1 ]
Zhu, Haibin [2 ]
Liu, Dongning [1 ,3 ]
机构
[1] Guangdong Univ Technol, Sch Comp Sci & Technol, Guangzhou 510006, Peoples R China
[2] Nipissing Univ, Collaborat Syst Lab CoSys Lab, North Bay, ON P1B 8L7, Canada
[3] City Univ Macau, Fac Data Sci, Macau 999078, Peoples R China
基金
加拿大自然科学与工程研究理事会; 中国国家自然科学基金;
关键词
Cooperation; conflict; and public interest relationships; E-CARGO; group multirole assignment with cooperation; and public interest factors (GMRACCF 1 PI); role-based collaboration (RBC); COLLABORATION;
D O I
10.1109/TCSS.2024.3374206
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In collaborative endeavors, there are always various types of cooperation, conflict, and public interest relationships. Taking home appliance production as a typical example, this article extends the group role assignment with the cooperation and conflict factors (GRACCF) model by newly adding public interest relationships to formalize the problem dealing with the above three collaboration relationships. By adding three different threshold parameters to represent cooperation, conflict as well as public interest relationships, we can make complex decisions based on the benefit values from different relationships. Through the discussion and analysis of large-scale experiments, different change trends are presented to help make certain adjustments and provide suggestions on the boundaries of these values in production and formulate appropriate production plans after determining these boundaries.
引用
收藏
页码:6112 / 6124
页数:13
相关论文
共 41 条
  • [1] Deep Reinforcement Learning for Truck-Drone Delivery Problem
    Bi, Zhiliang
    Guo, Xiwang
    Wang, Jiacun
    Qin, Shujin
    Liu, Guanjun
    [J]. DRONES, 2023, 7 (07)
  • [2] Robust Multi-Agent Reinforcement Learning Method Based on Adversarial Domain Randomization for Real-World Dual-UAV Cooperation
    Chen, Shutong
    Liu, Guanjun
    Zhou, Ziyuan
    Zhang, Kaiwen
    Wang, Jiacun
    [J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2024, 9 (01): : 1615 - 1627
  • [3] Teamwork in Integrated Design Projects: Understanding the Effects of Trust, Conflict, and Collaboration on Performance
    Chiocchio, Francois
    Forgues, Daniel
    Paradis, David
    Iordanova, Ivanka
    [J]. PROJECT MANAGEMENT JOURNAL, 2011, 42 (06) : 78 - 91
  • [4] Gao H., 2023, IEEE Truns. Consum. Electron., carly access, DOI [10.1109/TCE2023.3339633, DOI 10.1109/TCE2023.3339633]
  • [5] Com-DDPG: Task Offloading Based on Multiagent Reinforcement Learning for Information-Communication-Enhanced Mobile Edge Computing in the Internet of Vehicles
    Gao, Honghao
    Wang, Xuejie
    Wei, Wei
    Al-Dulaimi, Anwer
    Xu, Yueshen
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2024, 73 (01) : 348 - 361
  • [6] Neural Collaborative Learning for User Preference Discovery From Biased Behavior Sequences
    Gao, Honghao
    Wu, Yinchen
    Xu, Yueshen
    Li, Rui
    Jiang, Zhiping
    [J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, 2024, 11 (04): : 5068 - 5078
  • [7] Multi-agent collaboration for conflict management in residential demand response
    Golpayegani, Fatemeh
    Dusparic, Ivana
    Taylor, Adam
    Clarke, Siobhan
    [J]. COMPUTER COMMUNICATIONS, 2016, 96 : 63 - 72
  • [8] Haibin Zhu, 2012, International Journal of Agent Technologies and Systems, V4, P59, DOI 10.4018/jats.2012010104
  • [9] Towards a multi-party interaction framework: state-of-the-art review in sustainable operations management
    Hong, Zhaofu
    Zhang, Hongyan
    Gong, Yeming
    Yu, Yugang
    [J]. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2022, 60 (08) : 2625 - 2661
  • [10] Hui Z., 2011, PROC INT C E BUS E G, P1