An empirical study of collaborative capacity evaluation and scheduling optimization for a CPD project

Area of Science:

• Engineering Management
• Operations Research
• Computer Science

Background:

• Effective resource allocation is crucial for efficient collaborative product design.
• Project delays and cost overruns often stem from suboptimal team and task management.
• Existing scheduling models may not adequately capture the nuances of collaborative team dynamics.

Purpose of the Study:

• To develop a robust model for evaluating collaborative team capacity.
• To create an optimized task-team scheduling model for product design projects.
• To improve the efficiency and cost-effectiveness of collaborative product development.

Main Methods:

• Constructed a collaborative team capacity model using a 2-tuple linguistic method.
• Defined the matching degree between design tasks and collaborative teams.
• Developed an improved genetic algorithm incorporating simulated annealing, self-adaptive crossover, and mutation for scheduling optimization.

Main Results:

• The proposed model effectively evaluates team capacity and task-team matching.
• The improved genetic algorithm demonstrates superior performance in solving the scheduling model.
• A case study validated the practical applicability and effectiveness of the developed method.

Conclusions:

• The integrated approach of team capacity evaluation and task-team scheduling significantly enhances collaborative product design.
• The developed scheduling model and algorithm offer a practical solution for reducing development time and costs.
• This research provides a valuable framework for optimizing resource allocation in complex design projects.