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首页> 外文期刊>International Journal of Production Research >Cloud manufacturing service composition and optimal selection with sustainability considerations: a multi-objective integer bi-level multi-follower programming approach
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Cloud manufacturing service composition and optimal selection with sustainability considerations: a multi-objective integer bi-level multi-follower programming approach

机译：云制造服务组成和具有可持续性考虑因素的最佳选择：多目标整数双级多跟随器编程方法

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摘要

The process of service composition and optimal selection (SCOS) is an important issue in cloud manufacturing (CMfg). However, the current studies on CMfg and SCOS have generally focused on optimising the allocation of resources against quality of service (QoS), in terms of e.g. cost, quality, and time. They have seldom taken the perspective of sustainability into discussion, although sustainability is indispensable in the CMfg environment. Addressing this gap, we aim to (1) propose a comprehensive method to assess the sustainability of cloud manufacturing (SoM) in terms of the economic, environmental, and social aspects; (2) establish a multi-objective integer bi-level multi-follower programming (MOIBMFP) model to simultaneously maximise SoM and QoS from the perspectives of both platform operator and multiple service demanders; and (3) design a hybrid particle swarm optimisation algorithm to solve the proposed MOIBMFP model. The experimental results show that the proposed algorithm is more feasible and effective than the typical multi-objective particle swarm optimisation algorithm when solving the proposed model. In other words, the proposed model and algorithm suggest better alternatives to meet the needs of the platform operator and service demanders in the CMfg environment.
机译：服务成分和最优选择（SCOS）是云制造（CMFG）的重要问题。但是，目前关于CMFG和SCOS的研究通常集中在优化优化资源资源（QoS）的分配方面，就方面而言，成本，质量和时间。虽然可持续性在CMFG环境中不可或缺，但它们很少考虑可持续性的视角。解决这一差距，我们的目标是（1）提出了一种综合方法，以评估云制造（SOM）的可持续性，以方面的经济，环境和社会方面; （2）建立一个多目标整数双级多跟随器编程（MoibMFP）模型，以同时最大化SOM和QoS，从两台平台运营商和多个服务要求的角度来看; （3）设计混合粒子群优化算法，解决了所提出的MoibMFP模型。实验结果表明，当解决所提出的模型时，所提出的算法比典型的多目标粒子群优化算法更加可行且有效。换句话说，所提出的模型和算法表明了满足CMFG环境中平台运营商和服务要求的需求的更好的替代方案。

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