oderu: optimisation of semantic service-based processes in manufacturing

ODERU: Optimisation of Semantic Service-Based Processes in

Manufacturing

Luca Mazzola, Patrick Kapahnke, and Matthias Klusch

German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany

KESW conference 2017– Stettin (PL)

09/Nov/2017KEWS 2017 , Luca Mazzola

• Context

• Needs

• ODERU architecture and overview• Semantics for tasks and Services• Infrastructure and surrounding PEE• Constraint Optimization for QoS • Process service plans

• Two Applications• Machine Maintenance• OEE for Automotive Part Production

• Validation

Agenda


• SOA

• BPMN optimization

• XaaS

• Industry 4.0

• QoS Manufacturing Domain

Context


• ICT Integration for BPMN in Manufacturing

• Dynamic design and execution of BPMN• Adaptation to changing context• Service and Process Plan Optimization

• Functional and non-Functional requirements • Semantic models and KPI representation• QoS consideration and aggregation methods

• Effective composition of complete PSP• Support for run-time incremental re-planning

Needs for ODERU


Architecture - Semantics


• Process Task and Services semantically annotated• IOPE (Inputs/Outputs/Preconditions/Effects)

• Use of an OWL2 ontology, called CDM-Core• Hydraulic metal press maintenance• Car exhaust production

• BPMN extension for semantic annotation at the Task level

• OWL-S description of service into a repository

Architecture - Infrastructure


Architecture – COP for QoS


• BPMN extension for (COP) Constraint Optimization Problem definition, at the process level

• Based on a newly defined COPSE2 grammar• Usage of complex formulas• Adaptable type of constraints• User-definable optimization objective function

• Internally the COP is solved by the JaCoP package, but extensible to include any COP solver

• Result encoded back into the produced PSP in term of services selection and/or variable assignments

Architecture – PSP


2 steps: Service selection + Optimal Service composition

Application 1 (UC1)


• Maintenance of clutch-brake mechanism into metallic presses, operate by geographically distributed TAS team using part(s) provided by SP providers

• Objective @ design-time: provide a feasible combination (TAS team + SP provider) for common cases as fallback solution.

• Objective @ run-time: find one optimal combination that respects the constraints (guarantee, time to completion, max cost, TAS team schedule, SP availability, etc) minimising cost and time required

Application 2 (UC2)


• Optimization of car exhaust production by maximization of some OEE components for the robot cell involved

Objective @ design-time: compute the optimal independent parameters setting for the best compatible robot cell in the pool of candidates

• Objective @ run-time distinguished in two cases:a. searching for better setting after each batchb. changing the service used due to a robot

unavailability and find its optimal parameters

Validation


• Application 1: • (up to) 60% reduction of unscheduled machine breakdown• (up to) 15% reduction of the total machine breakdowns

(machine availability increased of ~18%)• (up to) 50% reduction in intervention time and • (up to) 25% reduction in costs for maintenance intervention

• Application 2:• increase speed to allocate production schedule to the

manufacturing assets (from the current 6 hours to 1 hour)• reduce significantly the time for engaging additional

manufacturing assets (from 6 months to 2 weeks)• scenario (A): increase aggregated OEE measure • from current 60% to 70%• scenario (B): increase OEE single components: “Quality”

from 55% to 75% and “Availability” from 60% to 70%

Ongoing activity:

expected results

Resources


Mazzola, L., Kapahnke, P., Vujic, M., & Klusch, M. (2016). CDM-Core: A Manufacturing Domain Ontology in OWL2 for Production and Maintenance. In KEOD (pp. 136-143).

Mazzola, L., Kapahnke, P., Waibel, P., Hochreiner, C., & Klusch, M. (2017). FCE4BPMN: On-demand QoS-based optimised process model execution in the cloud. In Proceedings of the 23rd ICE/IEEE ITMC Conference. IEEE.

Mazzola L., Kapahnke P., Klusch M. (2017) ODERU: Optimisation of Semantic Service-Based Processes in Manufacturing. In: Różewski P., Lange C. (eds) Knowledge Engineering and Semantic Web. KESW 2017. Communications in Computer and Information Science, vol 786. Springer, Cham

Mazzola L., Kapahnke P., Klusch M. (2017). Pattern-Based Semantic Composition of Optimal Process Service Plans with ODERU. In Proceedings of The 19th Int. Conference on Information Integration and Web-based Applications & Services, Salzburg, Austria, December 4–6, 2017 (iiWAS ’17), 10 pages. DOI: https://doi.org/10.1145/3151759.3151773

Mazzola L., and Kapahnke P. (2017). DLP: a Web-based Facility for Exploration and Basic Modification of Ontologies by Domain Experts. In Proceedings of The 19th Int. Conference on Information Integration and Web-based Applications & Services, Salzburg, Austria, December 4–6, 2017 (iiWAS ’17), 5 pages. DOI: https://doi.org/10.1145/3151759.3151816

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https://doi.org/10.1145/3151759.3151773

https://doi.org/10.1145/3151759.3151816

THANKS FOR THE ATTENTION

[email protected]@GMAIL.COM

http://www.crema-project.euH2020-RIA agreement 637066

https://www.linkedin.com/in/mazzolaluca/

The ODERU code can be found at:https://oderu.sourceforge.io/


http://www.crema-project.eu/

https://oderu.sourceforge.io/