ABSTRACT

Complex cooperative relationships between manufacturing equipment and services make such manufacturing networks have necessary research significance in network science. Under the prevailing background of mass customizing, the continuous coordination between manufacturing services (MSs) in the cloud platform is pulling the complex cooperative relationship between equipment and even enterprises, which constitutes a complex manufacturing network (CMN) from a macro perspective. In this paper, a credit-driven service configuration method is proposed, which is tactfully modeled on the reputation system of Uber. Under such circumstances, MSs could be evaluated combining theirexecution states and historical manufacturing behaviors and chosen as the most appropriate service under the current processing condition to tackle the manufacturing tasks according to customers’ requests accurately. Results in the proof-of-concept simulation show that credit is well integrated with the manufacturing process, and could be utilized to drive the CMN with a four-tier networked architecture on the whole, in which it is taken as the benchmark for the service-and-task smart matching in cases where customer requirements and manufacturing process stability are paramount. Credit also effectively assists MSs in exception self-organization and restrains the diffusion of the influence range of the deterioration of service credit when faced with service failures.

摘要

制造设备和服务之间复杂的协作关系使得这种制造网络在网络科学中具有必要的研究意义。在大规模定制盛行的背景下，云平台中制造服务（MS）之间的持续协同，拉动着设备乃至企业之间复杂的合作关系，从宏观上构成了复杂的制造网络（CMN）。在本文中，提出了一种信用驱动的服务配置方法，该方法巧妙地仿照了优步的信誉系统。在这样的情况下，MS可以结合其执行状态和历史制造行为进行评估，并选择为当前加工条件下最合适的服务，以准确解决客户要求的制造任务。概念验证模拟的结果表明，信用与制造过程很好地结合在一起，可以用来驱动CMN，整体上具有四层网络架构，其中以它作为服务的基准-在客户要求和制造过程稳定性至关重要的情况下进行任务智能匹配。信用也有效地帮助MS进行异常自组织，抑制服务信用恶化的影响范围在面临服务失败时的扩散。概念验证模拟的结果表明，信用与制造过程很好地结合在一起，可以用来驱动CMN，整体上具有四层网络架构，其中以它作为服务的基准-在客户要求和制造过程稳定性至关重要的情况下进行任务智能匹配。信用也有效地帮助MS进行异常自组织，抑制服务信用恶化的影响范围在面临服务失败时的扩散。概念验证模拟的结果表明，信用与制造过程很好地结合在一起，可以用来驱动CMN，整体上具有四层网络架构，其中以它作为服务的基准-在客户要求和制造过程稳定性至关重要的情况下进行任务智能匹配。信用也有效地帮助MS进行异常自组织，抑制服务信用恶化的影响范围在面临服务失败时的扩散。在客户需求和制造过程稳定性至关重要的情况下，它被用作服务和任务智能匹配的基准。信用也有效地帮助MS进行异常自组织，抑制服务信用恶化的影响范围在面临服务失败时的扩散。在客户需求和制造过程稳定性至关重要的情况下，它被用作服务和任务智能匹配的基准。信用也有效地帮助MS进行异常自组织，抑制服务信用恶化的影响范围在面临服务失败时的扩散。