Parameter analysis of objective function.

In recent years, intelligent robots have facilitated intelligent production, and a new type of problem (personnel–robot-position matching (PRPM)) has been encountered in personnel–position matching (PPM). In this study, a dynamic three-sided matching model is proposed to solve the PRPM problem in an intelligent production line based on man–machine collaboration. The first issue considered is setting the dynamic reference point, which is addressed in the information evaluation phase by proposing a method for setting the dynamic reference point based on the prospect theory. Another important issue involves multistage preference information integration, wherein a probability density function and a value function are introduced. Considering the attenuation of preference information in a time series, the attenuation index model is introduced to calculate the satisfaction matrix. Furthermore, a dynamic three-sided matching model is established. Additionally, a multi-objective decision-making model is established to optimize the matching of multiple sides (personnel, intelligent robots, and positions). Subsequently, the model is transformed into a single objective model using the triangular balance principle, which is introduced to obtain the final optimisation results in this modelling process. A case study is presented to illustrate the practicality of the dynamic three-sided matching model in intelligent environments. The results indicate that this model can solve the PRPM problem in an intelligent production line.近年来，智能机器人推动了智能化生产的发展，而在人员-岗位匹配（personnel–position matching, PPM）领域中，出现了一类新型问题——人员-机器人-岗位匹配（personnel–robot-position matching, PRPM）。本研究针对人机协同模式下智能生产线的PRPM问题，提出一种动态三边匹配模型。首先需要解决的核心问题是动态参考点的设定：在信息评估阶段，本文提出一种基于前景理论（prospect theory）的动态参考点设定方法。另一关键问题涉及多阶段偏好信息融合，研究中引入了概率密度函数与价值函数。考虑到时间序列下偏好信息的衰减特性，本文引入衰减指数模型计算满意度矩阵。此外，本文构建了动态三边匹配模型，同时建立多目标决策模型以优化三方主体（人员、智能机器人与岗位）的匹配关系。随后，通过三角平衡原理将该多目标模型转化为单目标模型，以此求解得到建模过程中的最终优化结果。最后，通过案例分析验证了动态三边匹配模型在智能场景中的实用性，结果表明该模型可有效解决智能生产线的PRPM问题。