article

Configuring Large Systems Using Generative Constraint Satisfaction

Authors:

Gerhard Fleischanderl,

Gerhard E. Friedrich,

Alois Haselböck,

Herwig Schreiner,

Markus StumptnerAuthors Info & Claims

IEEE Intelligent Systems, Volume 13, Issue 4

Pages 59 - 68

https://doi.org/10.1109/5254.708434

Published: 01 July 1998 Publication History

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Abstract

Generative constraint satisfaction solves problems using a constraint network that is extended during the configuration process. Generative constraints hold for all components of a given type and are used as generators for extending the configuration including the constraint network. We have successfully applied generative constraint satisfaction in Lava, which is now in use configuring the EWSD (Elektronisches Wählsystem Digital) digital switching systems developed and manufactured by Siemens AG. We implemented Lava using our domain-independent configuration tool, Cocos (configuration by constraint satisfaction).

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Comploi-Taupe RFrancescutto GSchenner GFelfernig AFuentes L(2022)Applying incremental answer set solving to product configurationProceedings of the 26th ACM International Systems and Software Product Line Conference - Volume B10.1145/3503229.3547069(150-155)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3503229.3547069
Popescu APolat-Erdeniz SFelfernig AUta MAtas MLe VPilsl KEnzelsberger MTran T(2022)An overview of machine learning techniques in constraint solvingJournal of Intelligent Information Systems10.1007/s10844-021-00666-558:1(91-118)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s10844-021-00666-5
Aameri BCheong HBeck JYoung BKitamura YSanfilippo E(2019)Towards an ontology for generative design of mechanical assembliesApplied Ontology10.3233/AO-19020714:2(127-153)Online publication date: 1-Jan-2019
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Published In

IEEE Intelligent Systems Volume 13, Issue 4

July 1998

86 pages

ISSN:1541-1672

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IEEE Educational Activities Department

United States

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Published: 01 July 1998

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Comploi-Taupe RFrancescutto GSchenner GFelfernig AFuentes L(2022)Applying incremental answer set solving to product configurationProceedings of the 26th ACM International Systems and Software Product Line Conference - Volume B10.1145/3503229.3547069(150-155)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3503229.3547069
Popescu APolat-Erdeniz SFelfernig AUta MAtas MLe VPilsl KEnzelsberger MTran T(2022)An overview of machine learning techniques in constraint solvingJournal of Intelligent Information Systems10.1007/s10844-021-00666-558:1(91-118)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s10844-021-00666-5
Aameri BCheong HBeck JYoung BKitamura YSanfilippo E(2019)Towards an ontology for generative design of mechanical assembliesApplied Ontology10.3233/AO-19020714:2(127-153)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.3233/AO-190207
Amand BCordy MHeymans PAcher MTemple PJézéquel JWeyns DPerrouin GWeyns D(2019)Towards Learning-Aided Configuration in 3D PrintingProceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems10.1145/3302333.3302338(1-9)Online publication date: 6-Feb-2019
https://dl.acm.org/doi/10.1145/3302333.3302338
Lazreg SCordy MCollet PHeymans PMosser SAtlee JBultan TWhittle J(2019)Multifaceted automated analyses for variability-intensive embedded systemsProceedings of the 41st International Conference on Software Engineering10.1109/ICSE.2019.00092(854-865)Online publication date: 25-May-2019
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Cordy MHeymans PHaddad HWainwright RChbeir R(2018)Engineering configurators for the retail industryProceedings of the 33rd Annual ACM Symposium on Applied Computing10.1145/3167132.3167352(2050-2057)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3167132.3167352
Felfernig AWalter RGalindo JBenavides DErdeniz SAtas MReiterer S(2018)Anytime diagnosis for reconfigurationJournal of Intelligent Information Systems10.1007/s10844-017-0492-151:1(161-182)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s10844-017-0492-1
(2017)Impact of product configuration systems on product profitability and costing accuracyComputers in Industry10.1016/j.compind.2017.03.00188:C(12-18)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1016/j.compind.2017.03.001
Tiihonen JFelfernig A(2017)An introduction to personalization and mass customizationJournal of Intelligent Information Systems10.1007/s10844-017-0465-449:1(1-7)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1007/s10844-017-0465-4
Falkner AFriedrich GHaselböck ASchenner GSchreiner H(2016)Twenty‐Five Years of Successful Application of Constraint Technologies at SiemensAI Magazine10.1609/aimag.v37i4.268837:4(67-80)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1609/aimag.v37i4.2688
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Constraint structure in constraint satisfaction problems

A dynamic constraint satisfaction approach for configuring structural products under mass customization

Periodic Constraint Satisfaction Problems: Tractable Subclasses

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