A three-tier programming model for service composition and optimal selection in cloud manufacturing

The process of service composition and optimal selection in cloud manufacturing (CMfg-SCOS) involves three types users: demanders, resource providers, platform operators. interests all users are a research focus CMfg-SCOS, as their participation the CMfg system directly affects efficiency long-term development CMfg. However, current on CMfg-SCOS rarely considers simultaneously, interest providers is not clearly defined, which lags behind reality Therefore, this study first proposes three-tier programming model that operators, providers. At lower level model, demanders decision makers, aiming to minimize time cost maximize quality. middle operators use flexibility face uncertain environments. upper level, enterprise surplus. Then, develops an improved fast nondominated sorting genetic algorithm with advancement inheritance (namely, a-i-NSGA-II) solve efficiently. Numerical experiments conducted found comparison art state algorithms, including original II (NSGA-II), multiobjective particle swarm optimization (MOPSO), spotted hyena optimizer (MOSHO), proposed a-i-NSGA-II has better diversity comprehensive performance at solution quality level. Furthermore, case actual production automobile fuel tank assembly verifies effectiveness algorithm.