An augmented Lagrangian coordination method for optimal allocation of cloud manufacturing services

With the rapid development of information and manufacturing technologies, cloud manufacturing (CMfg) was proposed and attracted wide attention. In CMfg, manufacturing service allocation (MSA) plays an important role in facilitating high-quality service management. MSA aims to optimize the allocation of services for manufacturing tasks. All-in-one (AIO) methods are widely used to obtain an optimal MSA result. However, the current AIO methods usually use one decision model so that it is difficult to maintain the autonomous decision rights of service providers. As a distributed optimization mechanism, augmented Lagrangian coordination (ALC) can offer an open-structure collaboration and allow participants to keep autonomous decision rights. In this paper, the MSA problem is partitioned into an ALC model based on the decision scope of service providers and solved in a loose coupling and distributed manner. A case study demonstrates the specific steps of ALC for solving the MSA problem. The results show the effectiveness and efficiency of ALC method in solving the MSA problem, as well as its promising feature in maintaining decision autonomy of a service provider.