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

Chenglei Zhang^1,2,3, Cunshan Zhang¹, Jiaojiao Zhuang²

¹School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China.

|August 6, 2021

Summary

This study introduces an optimized framework for cloud 3D printing (C3DP) order execution using a Pareto optimal set algorithm. It enhances resource allocation and service matching for distributed 3D printing equipment.

Keywords:

3D printing device resources HPSO baldwin effect cloud manufacturing (CMfg)multi-objective optimization

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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.