Part Features Oriented Optimizing of Manufacturing Resources for Cloud Manufacturing

Abstract

The research is targeted at the bottleneck technology of manufacturing resources optimizing configuration in cloud manufacturing, namely, the modeling of manufacturing resource and manufacturing task, the intelligent searching of manufacturing resource, and the optimizing of executive processing routes. Manufacturing resources are classified by manufacturing features. The framework of ontology concept is constructed, and the relationship among various concepts is made clear. Then the ontology model of manufacturing resources is constructed. The part features of manufacturing tasks are extracted, and the part features are expanded to include the information of manufacturing methods. Then the ontology model of manufacturing task is constructed. The concept tree method is adopted to compute the semantic similarity between manufacturing resource concept tree and manufacturing task concept tree, so to realize the mapping of manufacturing resource ontology to manufacturing task ontology, and the intelligent searching of manufacturing resources is accomplished. The best manufacturing resources are chosen from candidate resources for each job in processing route during the optimizing of executable processing routes. This problem belongs to a complicated multi objectives, multi choices, and multi constraints knapsack problem. A compromise based parallel multiple objective genetic algorithm is proposed to solve it. Finally, the compromised solution is obtained for the decision-maker.