Abstract
Focusing on service control factors, rapid changes in manufacturing environments, the difficulty of resource allocation evaluation, resource optimization for 3D printing services (3DPSs) in cloud manufacturing environments, and so on, an indicator evaluation framework is proposed for the cloud 3D printing (C3DP) order task execution process based on a Pareto optimal set algorithm that is optimized and evaluated for remotely distributed 3D printing equipment resources. Combined with the multi-objective method of data normalization, an optimization model for C3DP order execution based on the Pareto optimal set algorithm is constructed with these agents’ dynamic autonomy and distributed processing. This model can perform functions such as automatic matching and optimization of candidate services, and it is dynamic and reliable in the C3DP order task execution process based on the Pareto optimal set algorithm. Finally, a case study is designed to test the applicability and effectiveness of the C3DP order task execution process based on the analytic hierarchy process and technique for order of preference by similarity to ideal solution (AHP-TOPSIS) optimal set algorithm and the Baldwin effect.
Highlights
Intelligent algorithms are the most commonly used tool to solve NP-complete combination optimization problems
Pareto optimal optimization algorithm is objectively determined to the weight of each evaluation of set and indicator and construct a comprehensive analytic hierarchy process (AHP)-TOPSIS evaluation model based on a Pareto optimal set
The global economy is transforming from a product economy to a service economy
Summary
Intelligent algorithms are the most commonly used tool to solve NP-complete combination optimization problems. Typical simulated annealing and abut search have strong randomness and use only a single individual based on the iterative search, so the probability of finding a feasible solution is extremely low in a short time (iteration time) At present, they are mostly combined with other algorithms, but these are not suitable for solving optimization problems such as scheduling combinations. The intelligent optimization algorithm for a Pareto optimal set is based on the above-mentioned advantages and disadvantages [17,18,19] It is a newly improved local optimal solution or global optimal solution algorithm that is relatively suitable and has great potential for solving the problem of computing resource allocation
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