Framework and deployment of a cloud-based advanced planning and scheduling system |
| |
| Affiliation: | 1. Department of Industrial Engineering and Enterprise information, Tunghai University, Taichung, Taiwan, R.O.C.;2. Taiwan Semiconductor Manufacturing Company, Hsinchu City, Taiwan, R.O.C.;1. Xidian University, Xi''an, China;2. KTH Royal Institute of Technology, Stockholm, Sweden;3. The University of Auckland, Auckland, New Zealand;4. Beihang University, Beijing, China;1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;2. Department of Mechanical & Industrial Engineering, Louisiana State University, Baton Rouge, LA 70803, USA;1. School of Automation Science and Electrical Engineering, and Beijing Advanced Innovation Center for Big Data-based Precision Medicine, Beihang University, Beijing 100191, China.;2. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China. |
| |
| Abstract: | Many small and medium-sized manufacturing enterprises (SMEs) have already implemented enterprise resource planning (ERP) and manufacturing execution system (MES) and began to start the journey of cloud manufacturing; however, the high cost of hardware and software investment, implementation, and maintenance usually hinder SMEs from adopting an advanced planning and scheduling (APS) system. This paper aims to develop a cloud-based APS (C-APS) system framework, the service structure, and approach of deploying the C-APS system in a public cloud infrastructure platform and service provider or hybrid cloud platform. The package diagram is proposed for building the C-APS system's virtual factory model to improve modeling efficiency and data stability. The C-APS system is a cloud-based and object-oriented software; its simulation-based scheduling engine can generate the significant production and operations schedule, and has the characteristics of on-demand self-service, quickly expanding and adjusting to the virtual plant model. The C-APS system's application in a leading automotive part assembly company's printed circuit board production scheduling shows that the input planning data model is easy to maintain. The scheduling quality is high; the computing time is short and acceptable for practical application. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
