Cloud manufacturing service composition optimization based on reliability and credibility analysis

Abstract

In view of the impact of unstable manufacturing entity reliability and service reputation on the new-era manufacturing industry, the reliability and credibility of cloud manufacturing service were analyzed. Combining service reliability and credibility, composition complexity and synergy with execution time and cost, a new service quality evaluation model was constructed. The performance of service composition scheme was evaluated by calculating weighted relative deviation, and an Entropy Enhanced Particle Swarm Optimization (EEPSO) algorithm was proposed. The normal cloud model was introduced to improve the algorithm, which improved its global search ability in the early stage and local search accuracy in the later stage. Taking lifting assembly robot manufacturing task as an example, the validity of the proposed multi-objective optimization model for cloud manufacturing service composition and the feasibility of EEPSO algorithm were verified. Case studies showed that EEPSO had faster convergence speed and better comprehensive performance compared with SGA, CSBHC and so on.