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19 International Configuration Workshop 19th International Configuration Workshop Proceedings of the 19th International Configuration Workshop Edited by Linda L. ZHANG and Albert HAAG September 14 – 15, 2017 La Defense, France Organized by ISBN: 978-2-9516606-2-5 IESEG School of Management Socle de la Grande Arche 1 Parvis de La Défense 92044 Paris La Défense cedex France Linda L. ZHANG and Albert HAAG, Editors Proceedings of the 19th International Configuration Workshop September 14 – 15, 2017, La Défense, France Chairs Linda L ZHANG, IESEG School of Management, Lille-Paris, France Albert HAAG, Albert Haag – Product Management R&D, Germany Program Committee Michel ALDANONDO, Mines Albi, France Tomas AXLING, Tacton Systems AB, Sweden Andres BARCO, Universidad de San Buenaventura-Cali, Colombia Andreas FALKNER, Siemens AG, Austria Alexander FELFERNIG, Graz University of Technology, Austria Cipriano FORZA, Universita di Padova, Italy Gerhard FRIEDRICH, Alpen-Adria-Universitaet Klagenfurt, Austria Albert HAAG, Albert Haag – Product Management R&D, Germany Alois HASELBOECK, Siemens AG, Austria Petri HELO, University of Vassa, Finland Lothar HOTZ, HITeC e.V. / University of Hamburg, Germany Lars HVAM, Technical University of Denmark, Denmark Dietmar JANNACH, TU Dortmund, Germany Thorsten KREBS, Encoway GmbH, Germany Katrin KRISTJANSDOTTIR, Technical University of Denmark, Denmark Yiliu LIU, Norwegian University of Science and Technology, Norway Anna MYRODIA, Technical University of Denmark, Denmark Brian RODRIGUES, Singapore Management University, Singapore Sara SHAFIEE, Technical University of Denmark, Denmark Alfred TAUDES, Vienna University of Economics & Business, Austria Élise VAREILLES, Mines Albi, France Yue WANG, Hang Seng Management College, Hong Kong Linda ZHANG, IÉSEG School of Management, France Organizational Support Céline LE SUÜN, IÉSEG School of Management, France Julie MEILOX, IÉSEG School of Management, France Preface As a special design activity, product configuration greatly helps the specification of customized products. It has been a fruitful domain for applying and developing advanced artificial intelligence (AI) techniques. Configuration tasks demand powerful knowledge- representation formalisms and acquisition methods to capture the great variety and complexity of configurable product models. In addition, efficient reasoning is required to provide intelligent interactive behavior in contexts such as solution search, satisfaction of user preferences, personalization, optimization, and diagnosis. The main goal of the workshop is to promote high-quality research in all technical areas related to configuration. The workshop is of interest both for researchers working in the various fields of AI and product design as well as for industry representatives interested in the relationship between configuration technology and the business problem behind configuration and mass customization. It provides a forum for presentation of original methods and the exchange of ideas, evaluations, and experiences. As such, this year's Configuration Workshop again aims at providing a stimulating environment for knowledge-exchange among academia and industry and thus building a solid basis for further developments in the field. Furthermore, to encourage the continuous efforts, same as the past several workshops, the workshop this year sets a Best Student Paper Award (applicable to PhD candidates and bachelor and MSc students) and a Best Paper Award (applicable to all other participants than PhD candidates and bachelor and MSc students). The two papers are selected in a two-phase audience vote at the end of the workshop. Linda L ZHANG AND Albert HAAG Contents Configuration solving Learning constraint satisfaction heuristics for configuration problems 8 Giacomo Da Col and Erich Teppan Techniques for solving large-scale product configuration problems with ASP 12 Gottfried Schenner and Richard Taupe Assessing the complexity expressed in a variant table 20 Albert Haag ICONIC: INteractive CONstraInt-based configuration 28 Elise Vareilles, Helene Fargier, Michel Aldanondo and Paul Gaborit Tools and applications Features of 3D graphics in sales configuration 33 Petri Helo, Sami Kyllönen and Samuli Pylkkönen Increased accuracy of cost-estimation using product configuration systems 39 Jeppe Bredahl Rasmussen, Lars Hvam and Niels Henrik Mortensen Configuration and response to calls for tenders: an open bid configuration model Delphine Guillon, Abdourahim Sylla, Elise Vareilles, Michel Aldanondo, Eric Villeneuve, 46 Christophe Merlo, Thierry Coudert and Laurent Geneste Configuration knowledge representation and diagnosis Automated question generation from configuration knowledge Bases 54 Amal Shehadeh, Alexander Felfernig and Müslüm Atas ASP-based knowledge representations for IoT configuration scenarios Müslüm Atas, Paolo Azzoni, Andreas Falkner, Alexander Felfernig, Seda Polat 62 Erdeniz and Christoph Uran Cluster-based constraint ordering for direct diagnosis Muesluem Atas, Alexander Felfernig, Seda Polat Erdeniz, Stefan Reiterer, Amal Shehadeh 68 and Trang Tran Review and comparisons Modeling and configuration for Product-Service Systems: State of the art and future research 72 Daniel Schreiber, Paul Christoph Gembarski and Roland Lachmayer Complexity of configurators relative to integrations and field of application 80 Katrin Kristjansdottir, Sara Shafiee, Lars Hvam, Loris Battistello and Cipriano Forza Copyright © 2017 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors. Learning Constraint Satisfaction Heuristics for Configuration Problems Giacomo Da Col and Erich C. Teppan 1 Abstract. In this paper, we propose an approach for learning heuris- tive heuristics, solutions are out of reach for real-world sized prob- tics for constraint satisfaction problems in general and for configu- lem instances. In order to effectively traverse the large search space, ration problems in particular. The genetic algorithm based learning state-of-the-art constraint solvers offer a set of built-in problem- approach automatically derives variable ordering, value ordering and independent heuristics with which the solvers can be parametrized. pruning strategies for the exploitation by constraint solvers. We eval- Though, for hard real-world problems problem-dependent heuristics uate our approach with respect to the combined configuration prob- engineered by domain experts are typically needed (see for example lem, which is a generic configuration problem including sub prob- [22]). lems such as graph coloring or bin packing. The results show that one A particularity of configuration problems rooting in the dynamics of the best performing heuristics identified by our approach performs and flexibility of nowadays production environments is that they of- equally well compared to the expert heuristic defined in cooperation ten mutate over time. Thus, although not completely changing their with our project partners from Siemens. nature, configuration problem variants come up from time to time and replace old problem variants. Reasons for that can be chang- ing product portfolios, availability of newer technical components 1 Introduction with different technical requirements and/or extended possibilities, Configuration problems [14, 18] are classical planning problems change of the machinery in operation or legislation amendments. where elements have to be connected such that all user requirements A big defiance arising out of this is that already well-established are fulfilled and no technical constraints are violated. The configu- heuristics are not applicable any more and heuristic (re-)engineering ration of products and services, or more generally the configuration for adapting to a new problem variant is costly. Also, it is not sure of systems, is an important task and one of the major challenges in if the already existing heuristics can be adapted or if a new heuris- many production regimes such as mass customization, configure-to- tic must be designed from scratch. In order to cope with this is- order, or assembly-to-order [2, 19]. On the one hand, the basic goal sue, a learning approach for automatically creating effective problem is to provide customers with products and services that fulfill all their heuristics for configuration problems is highly desirable. requirements. On the other hand, these products and services shall be In this paper, we propose a heuristic learning framework for cre- offered at mass production efficiency. In order to fulfill these goals, ating problem-dependent heuristics for configuration problems ex- systems are assembled by pre-designed and pre-fabricated compo- pressed as CSPs. In particular, we describe how to represent the nents where such components themselves may be assembled by com- learning problem as a genetic algorithm. Evaluations are made with ponents. respect to the combined configuration problem (CCP), which consti- A solution for a configuration problem is a system description tutes a hard problem in the Answer Set Competition2. The CCP is (i.e. a configuration) that satisfies all requirements and contains all a generic configuration problem including bin packing, vertex color- the information needed for manufacturing or service provision in ing, assignment and path sub problems. an explicit, succinct, and simple to process format. To accomplish this, knowledge-based approaches are highly suitable and, among those, constraint-based approaches have a long and successful
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