To solve the problem of service composition optimization in the cloud manufacturing environment,by tak-ing the machining features as task granularity,a bi-layer optimization approach was proposed.The manufacturing resource supply graph and demand-feature graph were constructed to describe and store the information of suppliers and demanders respectively.Four indicators that were processing qualification rate,satisfaction evaluation values,on-time delivery rate and supply preference values were set sp as optimization indexes,and the first-level optimiza-tion was established through screening suppliers relied on the coefficient of variation algorithm for weighting the a-bove indexes.Taking total process cost,total process time and total carbon emission as the optimization objectives,and the priority relationship among the features were added as constraints,an optimization model was developed.An improved genetic algorithm NSGA-Ⅲ that incorporated sub-candidate set variation operators was used to solve the model,and the bi-layer optimization was established.With process flow sequence analysis to example results,the feasibility of the proposed approach and the effectiveness of the algorithm were verified.
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