Journal of Intelligent Information Systems

, Volume 49, Issue 1, pp 119–146 | Cite as

Building renovation adopts mass customization

Configuring insulating envelopes
  • Andrés F. Barco
  • Élise Vareilles
  • Paul Gaborit
  • Michel Aldanondo


This work is motivated by an industrial need of manufacturing façades insulating envelopes in order to reduce energy consumption in residential buildings. An insulating envelope is a configuration of a set of rectangular panels that respects a set of limitations. Due to the number of façades to be renovated and the number of possible configurations for a single façade, the envelope configuration is both a mass customization problem as well as a combinatorial one. The paper then introduces a decision support system based on the framework of constraint satisfaction, as it fits neatly the constrained nature of the problem. Two configuration tasks have been identified as prerequisite to envelopes configurations: (1) the configuration of a questionnaire for information inputs and (2) the configuration of a constraint satisfaction problem for each one of the façades to be renovated. The system architecture promotes maintenance, modularity and efficiency as different configuration tasks are divided into web-services. Conception and implementation of the massive building thermal renovation are then supported.


Building thermal renovation Massive product configuration Decision support system Constraint satisfaction Web-service architecture 



The authors wish to acknowledge the TBC Générateur d’Innovation company, the Millet and SyBois companies and all partners in the CRIBA project, for their contributions on recollecting buildings renovation information. Special thanks to the referees for their comments and to Philippe Chantry from École des Mines d’Albi for his contribution to the on-line system graphical interface and additional abstractions.


  1. Akin, O., Dave, B., & Pithavadian, S. (1992). Heuristic generation of layouts (hegel): based on a paradigm for problem structuring. Environment and Planning B: Planning and Design, 19(1), 33–59.CrossRefGoogle Scholar
  2. Aldanondo, M., Barco, A.F., Vareilles, E., Falcon, M., Gaborit, P., & Zhang, L. (2014). Towards a bim approach for a high performance renovation of apartment buildings. In Fukuda, S, Bernard, A, Gurumoorthy, B, & Bouras, A (Eds.) Product lifecycle management for a global market, IFIP advances in information and communication technology, (Vol. 442 pp. 21–30). Berlin Heidelberg: Springer. doi: 10.1007/978-3-662-45937-9_3.
  3. Barco, A.F., Vareilles, E., Aldanondo, M., & Gaborit, P. (2014). A recursive algorithm for building renovation in smart cities. In Foundations of intelligent systems, lecture notes in computer science, (Vol. 8502 pp. 144–153). Springer International Publishing. doi: 10.1007/978-3-319-08326-1_15
  4. Barco, A.F., Fages, J.G., Vareilles, E., Aldanondo, M., & Gaborit, P. (2015a). Open packing for facade-layout synthesis under a general purpose solver. In Pesant, G (Ed.) Principles and practice of constraint programming, lecture notes in computer science, (Vol. 9255 pp. 508–523): Springer International Publishing. doi: 10.1007/978-3-319-23219-5_36.
  5. Barco, A.F., Vareilles, E., Gaborit, P., & Aldanondo, M. (2015b). Industrialized building renovation: Manufacturing through a constraint-based on-line support system. In 2015 IEEE international conference on industrial engineering and engineering management (IEEM) (pp 947–951)  10.1109/IEEM.2015.7385789.
  6. Barták, R., Salido, M., & Rossi, F. (2010). Constraint satisfaction techniques in planning and scheduling. Journal of Intelligent Manufacturing, 21(1), 5–15. doi: 10.1007/s10845-008-0203-4.
  7. Baykan, C.A., & Fox, M.S. (1992). Artificial intelligence in engineering design (volume i). San Diego: Academic Press Professional, Inc.. chap WRIGHT: A Constraint Based Spatial Layout System, pp. 395–432.Google Scholar
  8. Beldiceanu, N., Carlsson, M., Poder, E., Sadek, R., & Truchet, C. (2007). A generic geometrical constraint kernel in space and time for handling polymorphic k-dimensional objects. In Bessière, C (Ed.) Principles and practice of constraint programming CP 2007, lecture notes in computer science. doi: 10.1007/978-3-540-74970-7_15, (Vol. 4741 pp. 180–194). Berlin Heidelberg: Springer.
  9. Beldiceanu, N., Carlsson, M., Demassey, S., & Poder, E. (2011). New filtering for the cumulative constraint in the context of non-overlapping rectangles. Annals of Operations Research, 184(1), 27–50. doi: 10.1007/s10479-010-0731-0.
  10. Blecker, T., & Abdelkafi, N. (2006). Complexity and variety in mass customization systems: analysis and recommendations. Management Decision, 44(7), 908–929. doi: 10.1108/00251740610680596.
  11. Center TEC (2011). Energy conservation handbook. The Energy Conservation Center, Japan.Google Scholar
  12. Council UGB (2013). New Construction Reference Guide. U.S. Green Building Council.Google Scholar
  13. Falkner, A., & Schreiner, H. (2014). Chapter 16 - siemens: configuration and reconfiguration in industry. In Felfernig, A, Hotz, L, Bagley, C, & Tiihonen, J (Eds.) Knowledge-based configuration, (pp. 199–210). Boston: Morgan Kaufmann. doi: 10.1016/B978-0-12-415817-7.00016-5.
  14. Falkner, A., Felfernig, A., & Haag, A. (2011). Recommendation technologies for configurable products. AI Magazine, 32, 99–108.Google Scholar
  15. Fan, L., Musialski, P., Liu, L., & Wonka, P. (2014). Structure completion for facade layouts. ACM Transactions on Graphics, 33(6), 210:1–210:11. doi: 10.1145/2661229.2661265.
  16. Felfernig, A., Hotz, L., Bagley, C., & Tiihonen, J. (2014). Knowledge-based configuration: from research to business cases. 1st edn. San Francisco: Morgan Kaufmann Publishers Inc.Google Scholar
  17. Flemming, U. (1990). Knowledge representation and acquisition in the LOOS system. Building and Environment, 25(3), 209–219. doi: 10.1016/0360-1323(90)90047-U.
  18. Flemming, U., & Woodbury, R. (1995). Software environment to support early phases in building design (seed): overview. Journal of Architectural Engineering, 1(4), 147–152. doi: 10.1061/(ASCE)1076-0431(1995)1:4(147).
  19. Fogliattoa, F. S., da Silveirab, G. J., & Borensteinc, D. (2012). The mass customization decade: an updated review of the literature. International Journal of Production Economics, 138(1), 14–25.CrossRefGoogle Scholar
  20. Gelle, E., & Weigel, R. (1996). Interactive configuration using constraint satisfaction techniques. In Second international conference on practical application of constraint technology, PACT-96 (pp. 37–44). Menlo Park: AAAI Press.Google Scholar
  21. Gent, I., & Walsh, T. (1999). Csplib: a benchmark library for constraints. In Jaffar, J (Ed.) Principles and practice of constraint programming - CP’99. Lecture notes in computer science, (Vol. 1713 pp. 480–481). Berlin Heidelberg: Springer.Google Scholar
  22. Hȯfling, B. (2014). Chapter 18 - encoway: from erp-based to sales-oriented configuration. In Felfernig, A, Hotz, L, Bagley, C, & Tiihonen, J (Eds.) Knowledge-based configuration, (pp. 219–227). Boston: Morgan Kaufmann. doi: 10.1016/B978-0-12-415817-7.00018-9.
  23. Jelle, B.P. (2011). Traditional, state-of-the-art and future thermal building insulation materials and solutions - properties, requirements and possibilities. Energy and Buildings, 43(10), 2549–2563. doi: 10.1016/j.enbuild.2011.05.015.
  24. Junker, U. (2006). Configuration. Chapter 24 of handbook of constraint programming (foundations of artificial intelligence). New York: Elsevier Science Inc.Google Scholar
  25. MacCarthy, B., Brabazon, P.G., & Bramham, J. (2003). Fundamental modes of operation for mass customization. International Journal of Production Economics, 85 (3), 289–304.CrossRefGoogle Scholar
  26. Medjdoub, B., & Yannou, B. (2000). Separating topology and geometry in space planning. Computer-Aided Design, 32(1), 39–61. doi: 10.1016/S0010-4485(99)00084-6.
  27. Mittal, S., & Falkenhainer, B. (1990). Dynamic constraint satisfaction problems. In AAAI (pp 25–32).Google Scholar
  28. Nica, I., Wotawa, F., Ochenbauer, R., Schober, C., Hofbauer, H., & Boltek, S. (2014). Chapter 19 - kapsch: reconfiguration of mobile phone networks. In Felfernig, A, Hotz, L, Bagley, C, & Tiihonen, J (Eds.) Knowledge-based configuration, (pp. 229–240). Boston: Morgan Kaufmann. doi: 10.1016/B978-0-12-415817-7.00019-0
  29. OpenRules, Inc (2013). Constraint programming solvers catalog. Available from
  30. Peng, Y., A Lu, D., & A Chen, Y. (2014). A constraint programming method for advanced planning and scheduling system with multilevel structured products. Discrete Dynamics in Nature and Society, 2014(Article ID 917685), 7 pages. doi: 10.1155/2014/917685
  31. Pérez-Lombard, L., Ortiz, J., & Pout, C. (2008). A review on buildings energy consumption information. Energy and Buildings, 40(3), 394–398. doi: 10.1016/j.enbuild.2007.03.007.
  32. Prud’homme, C., & Fages, J. (2013). An introduction to choco 3.0 an open source java constraint programming library. In CP solvers: modeling, applications, integration, and standardization. International workshop, Uppsala Sweden.Google Scholar
  33. Sabin, D., & Freuder, E. (1996). Configuration as composite constraint satisfaction. In Proc. artificial intelligence and manufacturing. Research planning workshop (pp. 153–161). AAAI Press.Google Scholar
  34. Schimpf, J., & Shen, K. (2012). Eclipse-from lp to clp. Theory Practice Log Program, 12(1-2), 127–156. doi: 10.1017/S1471068411000469.
  35. Schreiber, Y. (2010). Principles and practice of constraint programming. In CP 2010: 16th international conference, CP 2010, St. Andrews, Scotland, September 6-10, 2010. Proceedings, chap value-ordering heuristics: search performance vs. solution diversity, (pp. 429–444). Berlin: Springer. doi: 10.1007/978-3-642-15396-9_35.
  36. Schulte, C., Tack, G., & Lagerkvist, M.Z. (2010). Modeling and programming with gecode.Google Scholar
  37. Shikder, S., Price, A., & Mourshed, M. (2010). Interactive constraint-based space layout planning. In W070-special track 18th CIB world building congress (p. 112). Salford.Google Scholar
  38. Smith, B.M. (2006). Modelling. Chapter 11 of handbook of constraint programming (foundations of artificial intelligence). New York: Elsevier Science Inc.Google Scholar
  39. Soininen, T., Tiihonen, J., Männistö, T., & Sulonen, R. (1998). Towards a general ontology of configuration. Artificial Intelligence in Engineering Descriptive Analysis Manufacturing, 12(4), 357–372. doi: 10.1017/S0890060498124083.
  40. Soininen, T., Niemelä, I., Tiihonen, J., & Sulonen, R. (2000). Unified configuration knowledge representation using weight constraint rules. ECAI-2000 Workshop on Configuration.Google Scholar
  41. Teboul, O., Simon, L., Koutsourakis, P., & Paragios, N. (2010). Segmentation of building facades using procedural shape priors. In 2010 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 3105–3112). doi: 10.1109/CVPR.2010.5540068.
  42. Tiihonen, J., & Felfernig, A. (2010). Towards recommending configurable offerings. International Journal of Mass Customisation, 3(4), 389–406. doi: 10.1504/IJMASSC.2010.037652.
  43. Topaloglu, S., Salum, L., & Supciller, A. (2012). Rule-based modeling and constraint programming based solution of the assembly line balancing problem. Expert Systems with Applications, 39(3), 3484–3493. doi: 10.1016/j.eswa.2011.09.038.
  44. Triska, M. (2012). The finite domain constraint solver of swi-prolog. In Schrijvers, T, & Thiemann, P (Eds.) Functional and logic programming, lecture notes in computer science, (Vol. 7294 pp. 307–316). Berlin: Springer. doi: 10.1007/978-3-642-29822-6_24.
  45. Vareilles, E., Gaborit, P., Aldanondo, M., Carbonnel, S., & Steffan, L. (2012). Cofiade constraints filtering for aiding design. In Actes des neuviemes Journées Francophones de Programmation par Contraintes. Toulouse France.Google Scholar
  46. Vareilles, E., Barco Santa, A., Falcon, M., Aldanondo, M., & Gaborit, P. (2013). Configuration of high performance apartment buildings renovation: a constraint based approach. In 2013 IEEE international conference on industrial engineering and engineering management (IEEM) (pp. 684–688). doi: 10.1109/IEEM.2013.6962498.
  47. Wielemaker, J., Schrijvers, T., Triska, M., & Lager, T. (2010). Swi-prolog. Computing Research Repository abs/1011.5332.Google Scholar
  48. Wu, F., Yan, D.M., Dong, W., Zhang, X., & Wonka, P. (2014). Inverse procedural modeling of facade layouts. ACM Transactions on Graphics, 33(4), 121:1–121:10. doi: 10.1145/2601097.2601162.
  49. Xie, H., Henderson, P., & Kernahan, M. (2005). Modelling and solving engineering product configuration problems by constraint satisfaction. International Journal of Production Research, 43(20), 4455–4469. doi: 10.1080/00207540500142381.
  50. Yang, D., Dong, M., & Miao, R. (2008). Development of a product configuration system with an ontology-based approach. Computer-Aided Design, 40(8), 863–878. doi: 10.1016/j.cad.2008.05.004.
  51. Yang, D., Dong, M., & Chang, X. (2012). A dynamic constraint satisfaction approach for configuring structural products under mass customization. Engineering Applications of Artificial Intelligence, 25(8), 1723–1737.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Centre Génie IndustrielUniversité de Toulouse - Mines d’AlbiAlbi Cedex 09France

Personalised recommendations