Cloud manufacturing task decomposition method considering resource compatibility and competitiveness

To address the issue of disconnection between task decomposition and resource matching in cloud manufacturing, this paper proposes a task optimization decomposition method based on the HDBSCAN clustering algorithm. First, the task decomposition process is clarified, according to the task decomposition principles, the overall task is decomposed into indivisible meta-tasks. Secondly, a model of subtasks-resources-capabilities is constructed, transforming the ‘many-to-many’ mapping relationships into ‘one-to-one’ mapping relationships. Subsequently, considering task-resource compatibility, competitiveness, and the adaptive adjustment requirement of task granularity with the platform resource quantity, a hybrid approach combining genetic algorithm and HDBSCAN clustering algorithm is proposed to solve the model, resulting in the final cloud manufacturing task decomposition. Finally, the feasibility and effectiveness of the algorithm are verified by means of case studies. This method considers both task-resource compatibility and competition among resources, achieving optimization of task decomposition in cloud manufacturing, bridges the gap between task decomposition and resource allocation.