Evaluation of Cloud 3D Printing Order Task Execution Based on the AHP-TOPSIS Optimal Set Algorithm and the Baldwin...

Area of Science:

• Manufacturing Engineering
• Computer Science
• Operations Research

Background:

• Cloud manufacturing environments face challenges in resource allocation and service control.
• Rapid changes in manufacturing necessitate dynamic optimization of 3D printing services (3DPSs).
• Evaluating and optimizing remotely distributed 3D printing equipment resources is complex.

Purpose of the Study:

• To propose an indicator evaluation framework for cloud 3D printing (C3DP) order task execution.
• To develop an optimization model for C3DP order execution using a Pareto optimal set algorithm.
• To enhance the dynamic autonomy and distributed processing capabilities in C3DP.

Main Methods:

• Development of an indicator evaluation framework for C3DP order task execution.
• Construction of an optimization model based on the Pareto optimal set algorithm and data normalization.
• Integration of multi-objective optimization techniques for resource allocation.
• Application of analytic hierarchy process and technique for order of preference by similarity to ideal solution (AHP-TOPSIS) for evaluation.

Main Results:

• The proposed framework enables optimized and evaluated resource allocation for remotely distributed 3D printing equipment.
• The optimization model facilitates automatic matching and optimization of candidate services.
• The system demonstrates dynamic and reliable performance in the C3DP order task execution process.
• A case study validated the effectiveness of the AHP-TOPSIS-based Pareto optimal set algorithm.

Conclusions:

• The developed framework and model effectively address resource optimization challenges in cloud 3D printing.
• The Pareto optimal set algorithm enhances the efficiency and reliability of C3DP order execution.
• The approach provides a robust solution for managing distributed 3D printing resources in dynamic manufacturing environments.