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
09/Nov/2017KEWS 2017 , Luca Mazzola

• SOA
• BPMN optimization
• XaaS
• Industry 4.0
• QoS Manufacturing Domain
Context
09/Nov/2017KEWS 2017 , Luca Mazzola

• 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
09/Nov/2017KEWS 2017 , Luca Mazzola

Architecture - Semantics
09/Nov/2017KEWS 2017 , Luca Mazzola
• 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
09/Nov/2017KEWS 2017 , Luca Mazzola

Architecture – COP for QoS
09/Nov/2017KEWS 2017 , Luca Mazzola
• 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
09/Nov/2017KEWS 2017 , Luca Mazzola
2 steps: Service selection + Optimal Service composition

Application 1 (UC1)
09/Nov/2017KEWS 2017 , Luca Mazzola
• 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)
09/Nov/2017KEWS 2017 , Luca Mazzola
• 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 batch
b. changing the service used due to a robot
unavailability and find its optimal parameters

Validation
09/Nov/2017KEWS 2017 , Luca Mazzola
• 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
09/Nov/2017KEWS 2017 , Luca Mazzola
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
Forthcoming

THANKS FOR THE ATTENTION
QUESTIONS?
LUCA.MAZZOLA@DFKI.DE
MAZZOLA.LUCA@GMAIL.COM
http://www.crema-project.eu
H2020-RIA agreement 637066
https://www.linkedin.com/in/mazzolaluca/
The ODERU code can be found at:
https://oderu.sourceforge.io/
09/Nov/2017KEWS 2017 , Luca Mazzola