An Application of Context-sensitive Computing for Flexible Manufacturing System Optimization
|Kustantaja||Tampere University of Technology|
|Tila||Julkaistu - 26 toukokuuta 2017|
|Nimi||Tampere University of Technology. Publication|
Dynamic job (re)scheduling and dispatching are becoming an essential part of modern FMS controls. The foremost drive is to deal with the chaotic nature of the production environment while keeping plant performance indicators unaffected. Process plans in FMS need to consider several dynamic factors, like demand fluctuations, extreme product customizations and run time priority changes. To meet this plant level dynamism, complex control architectures are used to provide an automatic response to the unexpected events. These runtime responses deal with final moment change of the control parameters that eventually influences the key performance indicators (KPIs) like machine utilization rate and overall equipment effectiveness (OEE). In response, plant controls are moving towards more decentralized and adaptive architectures, promoting integration of different support applications. The applications aim to optimize the plant operations in terms of autonomous decision making, adaptation to sudden failure, system (re) configuration and response to unexpected events for global factory optimization.
The research work documented in this thesis presents the advantages of bridging the mentioned two domains of context-sensitive computing and FMS optimization, mainly to facilitate context management at factory floor for improved transparency and to better respond for real time optimization through context-based optimization support system.
This manuscript presents a context-sensitive optimization approach for FMS, considering machine utilization rate and overall equipment effectiveness (OEE) as the KPIs. Runtime contextual entities are used to monitor KPIs continuously to update an ontology-based context model, and subsequently convert it into business relevant information via context management. The delivered high level knowledge is further utilized by an optimization support system (OSS) to infer: optimal job (re) scheduling and dispatching, keeping a higher machine utilization rate at runtime. The proposed solution is presented as add-on functionality for FMS control, where a modular development of the overall approach provides the solution generic and extendable across other domains. The key components are functionally implemented to a practical FMS use-case within SOA and WS-based control architecture, resulting improvement of the machine utilization rate and the enhancement of the OEE at runtime.