An effective application of 3D cloud printing service quality evaluation in BM‐MOPSO

Addressing service control factors, rapid manufacturing environment change, difficulty of resource allocation evaluation, resource optimization of 3D cloud printing service in a cloud manufacturing environment, and other characteristics, this paper proposes an evaluation indicator system of innovative new product development 3D printing order task execution. The evaluation indicator has eight dimensional components, including Time (T), Quality of Service (Q), Matching (Mat), Reliability (R), Flexibility (Flex), Cost (C), Fault tolerance (Ft), and Satisfaction (Sa). It constructs a type of optimal selection model based on a Multi‐Agent 3D Cloud Printing Service Quality Evaluation and a framework of cloud service evaluation of an AHP‐TOPSIS evaluation model based on Pareto optimization, and it designs an algorithm involving hybrid multi‐objective particle swarm optimization (PSO) based on the Baldwin Effect Model. In addition, this paper verifies the effectiveness of the algorithm through an example and offers a case study designed to test its feasibility and effectiveness.

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