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Smart Process Optimization and Adaptive Execution with Semantic Services in Cloud Manufacturing ‡
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Luca Mazzola and Patrick Kapahnke worked at DFKI (German Research Center for Artificial Intelligence) during the ideation and development of the presented software solution.
‡
This manuscript is an extended version of the paper “ODERU: Optimisation of Semantic Service-Based Processes in Manufacturing” by Luca Mazzola, Patrick Kaphanke, and Matthias Klusch, Print ISBN: 978-3-319-69547-1, Online ISBN: 978-3-319-69548-8, doi:10.1007/978-3-319-69548-8_23, published in the proceedings of Knowledge Engineering and Semantic Web, Szczecin, Poland, 8–10 November 2017.
Version 1
: Received: 8 October 2018 / Approved: 8 October 2018 / Online: 8 October 2018 (12:11:53 CEST)
Version 2 : Received: 21 November 2018 / Approved: 22 November 2018 / Online: 22 November 2018 (05:29:31 CET)
Version 2 : Received: 21 November 2018 / Approved: 22 November 2018 / Online: 22 November 2018 (05:29:31 CET)
A peer-reviewed article of this Preprint also exists.
Mazzola, L.; Waibel, P.; Kaphanke, P.; Klusch, M. Smart Process Optimization and Adaptive Execution with Semantic Services in Cloud Manufacturing ‡. Information 2018, 9, 279. Mazzola, L.; Waibel, P.; Kaphanke, P.; Klusch, M. Smart Process Optimization and Adaptive Execution with Semantic Services in Cloud Manufacturing ‡. Information 2018, 9, 279.
Abstract
A new requirement for the manufacturing companies in Industry 4.0 is to be flexible with respect to changes in demands, requiring them to react rapidly and efficiently on the production capacities. Together with the trend to use Service-Oriented Architectures (SOA), this requirement induces a need for agile collaboration among supply chain partners, but also between different divisions or branches of the same company. In order to address this collaboration challenge, we~propose a novel pragmatic approach for the process analysis, implementation and execution. This~is achieved through sets of semantic annotations of business process models encoded into BPMN 2.0 extensions. Building blocks for such manufacturing processes are the individual available services, which are also semantically annotated according to the Everything-as-a-Service (XaaS) principles and stored into a common marketplace. The optimization of such manufacturing processes combines pattern-based semantic composition of services with their non-functional aspects. This is achieved by means of Quality-of-Service (QoS)-based Constraint Optimization Problem (COP) solving, resulting in an automatic implementation of service-based manufacturing processes. The produced solution is mapped back to the BPMN 2.0 standard formalism by means of the introduced extension elements, fully detailing the enactable optimal process service plan produced. This approach allows enacting a process instance, using just-in-time service leasing, allocation of resources and dynamic replanning in the case of failures. This proposition provides the best compromise between external visibility, control and flexibility. In this way, it provides an optimal approach for business process models' implementation, with a full service-oriented taste, by implementing user-defined QoS metrics, just-in-time execution and basic dynamic repairing capabilities. This paper presents the described approach and the technical architecture and depicts one initial industrial application in the manufacturing domain of aluminum forging for bicycle hull body forming, where the advantages stemming from the main capabilities of this approach are sketched.
Supplementary and Associated Material
https://www.mdpi.com/2078-2489/9/11/279: Published on: Information 2018, 9, 279
https://doi.org/10.3390/info9110279: doi:10.3390/info9110279
Keywords
Industry 4.0; XaaS; SemSOA; business process optimization; scalable cloud service deployment; process service plan just-in-time adaptation; BPMN partial fault tolerance
Subject
Computer Science and Mathematics, Information Systems
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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