Cloud manufacturing (CMfg) is a new networked manufacturing mode based on the big data and the Internet of Thing technologies, which can dynamically and flexibly allocate the manufacturing resources on demand. All the current researches and applications on CMfg focused on the factory manufacturing schema, the field manufacturing schema has not been concerned. Field manufacturing refers to the geographically dispersed manufacturing resources are arranged to different locations specified by customers to execute their manufacturing tasks, such as the assembly, measurement and maintenance of large equipments, this type of manufacturing is getting more and more applications with the increase of product complexity and the trend of manufacturing industry servicizing. At present, the massive complicated field manufacturing tasks bring urgent needs on the dynamic organization and global planning of the dispersed manufacturing resources, which can be achieved by the network technology. To this end, this paper studies the scheduling of field manufacturing resources in CMfg environment. Firstly, the detailed process of the field service resource scheduling in CMfg environment (FSRS-CMfg) is designed based on analyzing the practical field manufacturing process. After presenting the assumptions and constraints of field manufacturing, the optimization model for the FSRS-CMfg problem is established, in which the overall quality of service (QoS) is used as the optimization objective and its evaluation indicators and aggregation method are elaborately designed. Then, to solve the above optimization model, the encoding and decoding methods are designed and the discrete search operators are developed. For improving the solving speed and accuracy, a multi-population competitive-cooperative grey wolf optimizer (MPCCGWO) is built to avoid trapping in local optimum. Finally, two experiments are carried out to verify the correctness and effectiveness of the proposed model and algorithm. Results show that the overall QoS of the field manufacturing resource scheduling scheme can be markedly improved based on the FSRS-CMfg model, and MPCCGWO possesses higher convergence precision than the commonly-used intelligent optimization algorithms without obvious increase of time consumption, so it is suitable for solving the problems with large solution spaces such as the FSRS-CMfg problem.

云制造（CMfg）是一种基于大数据和物联网技术的新型网络化制造模式，可以根据需要动态灵活地分配制造资源。当前关于CMfg的所有研究和应用都集中在工厂制造模式上，而现场制造模式则未被关注。现场制造是指将地理上分散的制造资源布置到客户指定的不同位置，以执行其制造任务，例如大型设备的组装，测量和维护，这种制造随着产品的增加而得到越来越多的应用复杂性和制造业服务的趋势。现在，庞大而复杂的现场制造任务给分散的制造资源的动态组织和全局规划提出了迫切的需求，这可以通过网络技术来实现。为此，本文研究了CMfg环境中的现场制造资源调度。首先，在分析实际制造过程的基础上，设计了CMfg环境下的现场服务资源调度的详细过程（FSRS-CMfg）。在提出现场制造的假设和约束条件之后，建立了针对FSRS-CMfg问题的优化模型，其中以服务的总体质量（QoS）为优化目标，并精心设计了其评估指标和汇总方法。然后，要解决上述优化模型，设计了编码和解码方法，并开发了离散搜索算子。为了提高求解速度和精度，构建了多种群竞争合作式灰狼优化器（MPCCGWO），以避免陷入局部最优状态。最后，通过两个实验验证了所提模型和算法的正确性和有效性。结果表明，基于FSRS-CMfg模型，可以显着提高现场制造资源调度方案的整体QoS，并且MPCCGWO的收敛精度高于常用的智能优化算法，且时间消耗没有明显增加，因此很适合用于解决解决方案空间较大的问题，例如FSRS-CMfg问题。为了提高求解速度和精度，构建了多种群竞争合作式灰狼优化器（MPCCGWO），以避免陷入局部最优状态。最后，通过两个实验验证了所提模型和算法的正确性和有效性。结果表明，基于FSRS-CMfg模型，可以显着提高现场制造资源调度方案的整体QoS，并且MPCCGWO的收敛精度高于常用的智能优化算法，且时间消耗没有明显增加，因此很适合用于解决具有较大解空间的问题，例如FSRS-CMfg问题。为了提高求解速度和精度，构建了多种群竞争合作式灰狼优化器（MPCCGWO），以避免陷入局部最优状态。最后，通过两个实验验证了所提模型和算法的正确性和有效性。结果表明，基于FSRS-CMfg模型，可以显着提高现场制造资源调度方案的整体QoS，并且MPCCGWO的收敛精度高于常用的智能优化算法，且时间消耗没有明显增加，因此很适合用于解决解决方案空间较大的问题，例如FSRS-CMfg问题。进行了两个实验，验证了所提模型和算法的正确性和有效性。结果表明，基于FSRS-CMfg模型，可以显着提高现场制造资源调度方案的整体QoS，并且MPCCGWO的收敛精度高于常用的智能优化算法，且时间消耗没有明显增加，因此很适合用于解决解决方案空间较大的问题，例如FSRS-CMfg问题。进行了两个实验，验证了所提模型和算法的正确性和有效性。结果表明，基于FSRS-CMfg模型，可以显着提高现场制造资源调度方案的整体QoS，并且MPCCGWO的收敛精度高于常用的智能优化算法，且时间消耗没有明显增加，因此很适合用于解决解决方案空间较大的问题，例如FSRS-CMfg问题。