Internet-of-Things-Enabled Smart Production Logistics Execution System Based on Cloud Manufacturing

Abstract

With the rapid development of cloud computer concept and technologies, more and more cloud-based business mode and practical applications are emerging in industrial environments, including cloud manufacturing and cloud logistics. Such cloud systems integrate the distributed resources and make best use of them to fulfill dynamic tasks in an optimal way. This paper will demonstrate a simple yet practical application of a cloud-based production logistics (PL) management system (C-PLES) developed under Internet-of-Things (IoT) environment. It collects both real-time process dynamics and resource statues from a ubiquitous PL environment to realize dynamic distributed capability matching. The systematic combination of IoT and cloud system enables the distributed PL terminal resources with uncertainties (e.g. availabilities and locations) to be optimally accessed and assigned to fulfill the real-time PL requirements generated from the dynamic production processes. It also enables the distributed execution data to be centrally managed and seamlessly switched among the dynamically accessed resources.

Scheduling in cloud manufacturing: state-of-the-art and research challenges

“…Scheduling in cloud manufacturing is more complex and dynamic compared with preceding manufacturing models. The complexity of scheduling in cloud manufacturing comes from the involvement of wide-area logistics and the complexity of resources (Qu et al 2014). Manufacturing enterprises registered in a cloud manufacturing system are distributed in wide areas, which makes logistics indispensable for scheduling in cloud manufacturing Zhong et al 2016).…”

An integrated framework for active discovery and optimal allocation of smart manufacturing services

“…Many smart manufacturing paradigms are proposed and the key technologies of which are further investigated as summarized in Table 1. Capturing real-time data of manufacturing resources and making better-informed enterprises decisions (Bi et al, 2014;Zhang et al, 2015); improving energy-aware production management (Shrouf and Miragliotta, 2015), smart production-logistics (Qu et al, 2014), supply chain management (Ben-Daya et al, 2019), production planning and scheduling (Tian et al, 2019; Cloud manufacturing IoT, cloud computing Providing on-demand capabilities of distributed manufacturing resources for meeting personalized manufacturing requirements (Tao et al, 2014;Xu, 2012;Zhang et al, 2017b) Edge and Fog-based IoT, edge and Enabling the application of business logic between manufacturing fog computing the downstream data of services and the upstream data of devices in the smart factory (Chen et al, 2018;Wu et al, 2017) Social manufacturing IoT, CPS…”

Cloud-based cyber-physical systems and quality of services

“…The results showed that the system offers enhanced capabilities of monitoring and analyzing the production flow and also leads to decreased costs, defect parts and increased production efficiency through better workload balancing. An IoT approach to cloud-based production logistics planning is introduced by Qu et al (2014). The proposed approach was implemented in a paint factory which consisted of 19 palletizing points and two warehouses.…”