A QoS and sustainability-driven two-stage service composition method in cloud manufacturing: combining clustering and bi-objective optimization

Abstract

Manufacturing service composition (MSC) is a core technology in cloud manufacturing (CMfg), which has been intensively studied to find an optimal composite service with the best quality of service (QoS). With the continuous expansion of CMfg platforms, the difficulty of MSC is gradually increasing. Large-scale platforms have put forward higher requirements for combination efficiency, and its open and dynamic environment makes service QoS exhibit strong uncertainty, leading to reliability issues of MSC. Meanwhile, the increased number of services and users makes it necessary for the platform to consider the sustainability issue, including economic, environmental, and social aspects, based on an operations management perspective. However, current studies only consider part of efficiency, reliability, and sustainability as optimization objectives in MSC allocation models, and do not take them into account simultaneously in an integrated manner. Therefore, this study proposes a two-stage method integrating clustering and multi-objective optimization for reliable and sustainable MSC allocation. Specifically, in the first stage, the K-means clustering technique and the QoS stability-based service pruning mechanism are integrated into the service clustering process to improve the reliability of candidate services and reduce the search space of combinations. In the second stage, a multi-objective optimization model with maximizing QoS and sustainability is proposed to find the optimal MSC, and the fast non-dominated sorting genetic algorithm is adopted to solve the model. Finally, a case study of the actual production of a customized automated guided vehicle verifies the effectiveness of the proposed two-stage method.