Dynamic three-sided matching model for personnel-robot-position matching problem in intelligent environments

Area of Science:

• Intelligent Systems
• Operations Research
• Industrial Engineering

Background:

• Intelligent robots are transforming production lines.
• New challenges arise in matching personnel, robots, and positions (PRPM).
• Existing personnel-position matching (PPM) models are insufficient for intelligent production.

Purpose of the Study:

• To propose a dynamic three-sided matching model for PRPM in intelligent production.
• To optimize the matching of personnel, intelligent robots, and positions.
• To enhance man-machine collaboration in intelligent manufacturing.

Main Methods:

• Developed a dynamic reference point setting method based on prospect theory.
• Integrated multistage preference information using probability density and value functions.
• Introduced an attenuation index model and a multi-objective decision-making model, transformed via the triangular balance principle.

Main Results:

• A novel dynamic three-sided matching model (PRPM) was established.
• The model effectively optimizes the matching of personnel, robots, and positions.
• A case study demonstrated the model's practicality in intelligent production environments.

Conclusions:

• The dynamic three-sided matching model successfully addresses the PRPM problem.
• The proposed model enhances efficiency and collaboration in intelligent production lines.
• This research offers a robust solution for complex man-machine-position integration.