A machine tool matching method in cloud manufacturing using Markov Decision Process and cross-entropy

Abstract

Abstract Cloud Manufacturing (CMfg) is a state-of-the-art manufacturing paradigm implementing the concept of service-oriented manufacturing. Machine tools are one kind of the critical manufacturing resources in Cloud Manufacturing, however machine tool matching is still immature owning to customization manufacturing service demands from users and various disturbing factors in production. This paper proposes a machine tool matching method for dealing with a single Cloud Manufacturing task with complex machine tool application demands. In this method, the demands of machine tools and themselves are described and evaluated based on a universal framework to obtain candidate resource groups satisfying local requirements of sub-demands. Then, a series of Markov Decision Processes (MDP) is established, which take the minimal service cost as optimal object to meet global requirements, and a cross-entropy based algorithm is used to solve the optimal object. Finally, simulation experiments are conducted to validate the usability and superiority in efficiency of the proposed method.