A Pareto Optimal Service Selection Approach for Cloud Manufacturing

Abstract The cloud manufacturing model plays a crucial role in the deep integration of information technology and the manufacturing industry, which dynamically adjusts manufacturing processes. If the optimization of collaborative manufacturing resources does not fully consider the intrinsic relationship among tasks, service resources, and dynamic constraints, there will be a significant deviation between the anticipated production process and the reality. To overcome these shortcomings, this paper constructs a Pareto optimal service selection model with multiple production constraints suitable for a cloud manufacturing environment. The Pareto dominance strategy is applied to filter out non-candidate solutions, subsequently, the tournament selection strategy is employed to select services from the Pareto archive set based on their fitness values, which enhances the diversity of solutions and improves multi-target search capabilities. Application examples confirm that the proposed algorithm outperforms other comparison algorithms in performance.