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