Research on concurrent negotiation particle swarm optimization model of supply chain

Abstract

Abstract: For the production-marketing collaboration among supply chain enterprises, the multi-agent technology is applied to two-stage supply chain, and the concurrent negotiation model with incomplete information is proposed. The particle swarm optimization can update belief valve of the negotiation agents to support the continuous negotiation. The simulation results show its feasibility and effectiveness, and it has advantages in terms of negotiation efficiency, negotiation time and success rate compared to the other negotiation model.

Key words: concurrent negotiation, particle swarm optimization, Parzen windows estimation, multi-agent, supply chain

摘要： 针对供应链企业之间的产销协同问题，将多Agent技术运用于二级供应链中，建立一种不完全信息约束下的并发协商模型。基于粒子群优化的协调策略可以在协商过程中更新协商Agents信念值进而支持连续协商。仿真结果表明此模型的可行性和有效性，与其他并发协商模型相比，该模型在协商结果效用、协商时间、协商成功率方面具有优势。

关键词: 并发协商, 粒子群优化, Parzen窗估计, 多Agent, 供应链

WU Yuying, RAO Yushu, JIANG Guorui. Research on concurrent negotiation particle swarm optimization model of supply chain[J]. Computer Engineering and Applications, 2016, 52(4): 229-233.

武玉英，饶毓书，蒋国瑞. 供应链并发协商粒子群优化模型研究[J]. 计算机工程与应用, 2016, 52(4): 229-233.

[1]	HUANG Yilei, XIA Zhijie, WANG Yiming. Research on False Information Dissemination Model of Strong-Ties Social Media [J]. Computer Engineering and Applications, 2021, 57(9): 126-133.
[2]	SHI Chuntian, ZENG Yanyang, HOU Shouming. Summary of Application of Swarm Intelligence Algorithms in Image Segmentation [J]. Computer Engineering and Applications, 2021, 57(8): 36-47.
[3]	LI Jingxing, YANG Youlong. Feature Selection of Markov Blanket for High Dimensional Data [J]. Computer Engineering and Applications, 2021, 57(6): 58-66.
[4]	ZHU Jiaying, GAO Maoting. AUV Path Planning Based on Particle Swarm Optimization and Improved Ant Colony Optimization [J]. Computer Engineering and Applications, 2021, 57(6): 267-273.
[5]	HU Xiaomin, WANG Mingfeng, ZHANG Shourong, LI Min. New Differential Evolution with Particle Swarm Optimization Algorithm for Text Clustering [J]. Computer Engineering and Applications, 2021, 57(4): 61-67.
[6]	CHEN Shiming, LIN Zipeng, GAO Yanli, PEI Huiqin. Heterogeneous Group Consensus Under Adaptive Coupling Weights [J]. Computer Engineering and Applications, 2021, 57(4): 231-235.
[7]	ZHANG Fang, WU Jie, YANG Yue. Emission Reduction and Coordination Decision in Cold Chain Logistics Service Supply Chain Considering Risk Aversion [J]. Computer Engineering and Applications, 2021, 57(24): 259-267.
[8]	WANG Jiayue, SHI Li. Research on Path Optimization and Charging Strategy of Electric Refrigerated Vehicle Considering Cost of Battery Capacity Degradation [J]. Computer Engineering and Applications, 2021, 57(24): 283-289.
[9]	CHEN Wanfen, WANG Yujia, LIN Weixing. Heterogeneous Ensemble Surrogate Assisted Multi-objective Particle Swarm Optimization Algorithm [J]. Computer Engineering and Applications, 2021, 57(23): 71-80.
[10]	DU Shouxin, WU Tao. Study on Application of Double Population Hybrid Genetic Algorithm in Cut Order Planning [J]. Computer Engineering and Applications, 2021, 57(22): 182-189.
[11]	LIN Weixing, WANG Yujia, CHEN Wanfen, LIANG Haina. High-Dimensional Data Feature Selection Algorithm Based on Multifactor Particle Swarm Optimization [J]. Computer Engineering and Applications, 2021, 57(22): 199-207.
[12]	WANG Jun, CAO Lei, CHEN Xiliang, LAI Jun, ZHANG Legui. Overview on Reinforcement Learning of Multi-agent Game [J]. Computer Engineering and Applications, 2021, 57(21): 1-13.
[13]	LI Zhentao, FENG Yuanzhen, WANG Zhengxin. Fixed-Time Bipartite Consensus of Multi-agent Systems via Event-Triggered Control [J]. Computer Engineering and Applications, 2021, 57(21): 80-86.
[14]	KUANG Liqun, LI Siyuan, FENG Li, HAN Xie, XU Qingyu. Application of Deep Reinforcement Learning Algorithm on Intelligent Military Decision System [J]. Computer Engineering and Applications, 2021, 57(20): 271-278.
[15]	TIAN Yang, CHEN Zhigang, SONG Xinxia, LI Tianming. Overview of Blockchain Application in Supply Chain Management [J]. Computer Engineering and Applications, 2021, 57(19): 70-83.

Research on concurrent negotiation particle swarm optimization model of supply chain

供应链并发协商粒子群优化模型研究

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics