Skip to main content
SpringerLink
Log in

Automated Design of Elevator Systems: Experimenting with Constraint-Based Approaches

  • Conference paper
  • First Online:
AIxIA 2021 – Advances in Artificial Intelligence (AIxIA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13196))

  • 838 Accesses

Abstract

System configuration and design is a well-established topic in AI. While many successful applications exist, there are still areas of manufacturing where AI techniques find little or no application. We focus on one such area, namely building and installation of elevator systems, for which we are developing an automated design and configuration tool. The questions that we address in this paper are: (i) What are the best ways to encode some subtasks of elevator design into constraint-based representations? (ii) What are the best tools available to solve the encodings? We contribute an empirical analysis to address these questions in our domain of interest, as well as the complete set of benchmarks to foster further research.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    We run Chuffed v0.10.3, OR-Tools v7.8, ECL\(^i\)PS\(^e\) v7.0, CPLEX v12.7, Gurobi v9.0.1, z3 v4.8.7 and OptiMathSat v1.7.0.1. z3 and OptiMathSat do not generate proofs of their results in the (default) configuration that we tested.

  2. 2.

    All tests run on a PC equipped with an Intel® Core™ i7-6500U dual core CPU @ 2.50 GHz, featuring 8 GB of RAM and running Ubuntu Linux 16.04 LTS 64 bit.

References

  1. Annunziata, L., Menapace, M., Tacchella, A.: Computer intensive vs. heuristic methods in automated design of elevator systems. In: Proceedings of European Conference on Modelling and Simulation, ECMS 2017, Budapest, Hungary, 23–26 May 2017, pp. 543–549 (2017)

    Google Scholar 

  2. Ansótegui, C., Bofill, M., Palahí, M., Suy, J., Villaret, M.: Solving weighted CSPs with meta-constraints by reformulation into satisfiability modulo theories. Constraints Int. J. 18(2), 236–268 (2013)

    Article  MathSciNet  Google Scholar 

  3. Bacchus, F.: GAC via unit propagation. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 133–147. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74970-7_12

    Chapter  Google Scholar 

  4. Barret, C., Fontaine, P., Tinelli, C.: The SMT-LIB standard - version 2.6 (2017). http://smtlib.cs.uiowa.edu/papers/smt-lib-reference-v2.6-r2017-07-18.pdf

  5. Bofill, M., Palahí, M., Suy, J., Villaret, M.: Solving constraint satisfaction problems with SAT modulo theories. Constraints Int. J. 17(3), 273–303 (2012)

    Article  MathSciNet  Google Scholar 

  6. Chang, K.H.: Multiobjective optimization and advanced topics, Chapter 19. In: Chang, K.H. (ed.) e-Design, pp. 1105–1173. Academic Press, Boston (2015)

    Google Scholar 

  7. Chu, G.: Improving combinatorial optimization. In: Twenty-Third International Joint Conference on Artificial Intelligence (2013)

    Google Scholar 

  8. Contaldo, F., Trentin, P., Sebastiani, R.: From MiniZinc to optimization modulo theories, and back. In: Hebrard, E., Musliu, N. (eds.) CPAIOR 2020. LNCS, vol. 12296, pp. 148–166. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58942-4_10

    Chapter  Google Scholar 

  9. Demarchi, S., Menapace, M., Tacchella, A.: Automating elevator design with satisfiability modulo theories. In: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), pp. 26–33. IEEE (2019)

    Google Scholar 

  10. Gu, Z., Rothberg, E., Bixby, R.: Gurobi optimization (2019). http://www.gurobi.com/

  11. IBM: IBM ILOG CPLEX optimization studio (2017) CPLEX users manual, version 12.7 (2017)

    Google Scholar 

  12. Marcus, S., Stout, J., McDermott, J.: VT: an expert elevator designer that uses knowledge-based backtracking. AI Mag. 8(4), 41–41 (1987)

    Google Scholar 

  13. Marriott, K., Stuckey, P.J., Koninck, L., Samulowitz, H.: A minizinc tutorial (2014)

    Google Scholar 

  14. McDermott, J.: R1: the formative years. AI Mag. 2(2), 21–21 (1981)

    Google Scholar 

  15. Mittal, S., Falkenhainer, B.: Dynamic constraint satisfaction. In: Proceedings Eighth National Conference on Artificial Intelligence, pp. 25–32 (1990)

    Google Scholar 

  16. Mittal, S., Frayman, F.: Towards a generic model of configuraton tasks. In: IJCAI, vol. 89, pp. 1395–1401 (1989)

    Google Scholar 

  17. de Moura, L., Bjørner, N.: Z3: an efficient SMT solver. In: Ramakrishnan, C.R., Rehof, J. (eds.) TACAS 2008. LNCS, vol. 4963, pp. 337–340. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78800-3_24

    Chapter  Google Scholar 

  18. Nethercote, N., Stuckey, P.J., Becket, R., Brand, S., Duck, G.J., Tack, G.: MiniZinc: towards a standard CP modelling language. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 529–543. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74970-7_38

    Chapter  Google Scholar 

  19. Perron, L., Furnon, V.: OR-Tools (2020). https://developers.google.com/optimization/

  20. Quéva, M.: A Framework for constraint-programming based configuration. DTU Informatics (2011)

    Google Scholar 

  21. Schimpf, J., Shen, K.: ECLiPSe - from LP to CLP. Theory Pract. Logic Program. 12(1–2), 127–156 (2012). https://doi.org/10.1017/S1471068411000469

    Article  MathSciNet  MATH  Google Scholar 

  22. Sebastiani, R., Trentin, P.: On optimization modulo theories, MaxSMT and sorting networks. In: Legay, A., Margaria, T. (eds.) TACAS 2017. LNCS, vol. 10206, pp. 231–248. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-662-54580-5_14

    Chapter  Google Scholar 

  23. Sebastiani, R., Trentin, P.: OptiMathSAT: a tool for optimization modulo theories. J. Autom. Reason. 64(3), 423–460 (2018). https://doi.org/10.1007/s10817-018-09508-6

    Article  MathSciNet  MATH  Google Scholar 

  24. Stumptner, M., Friedrich, G.E., Haselbok, A.: Generative constraint-based configuration of large technical systems. Artif. Intell. Eng. Des. Anal. Manuf. 12(4), 307–320 (1998). https://doi.org/10.1017/S0890060498124046

    Article  Google Scholar 

  25. Zhang, L.L.: Product configuration: a review of the state-of-the-art and future research. Int. J. Prod. Res. 52(21), 6381–6398 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Università degli Studi di Genova, Viale Causa 13, 16145, Genova, Italy

    Stefano Demarchi, Marco Menapace & Armando Tacchella

Authors
  1. Stefano Demarchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marco Menapace
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Armando Tacchella
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stefano Demarchi .

Editor information

Editors and Affiliations

  1. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Stefania Bandini

  2. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Francesca Gasparini

  3. Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genova, Italy

    Viviana Mascardi

  4. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Matteo Palmonari

  5. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Giuseppe Vizzari

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Demarchi, S., Menapace, M., Tacchella, A. (2022). Automated Design of Elevator Systems: Experimenting with Constraint-Based Approaches. In: Bandini, S., Gasparini, F., Mascardi, V., Palmonari, M., Vizzari, G. (eds) AIxIA 2021 – Advances in Artificial Intelligence. AIxIA 2021. Lecture Notes in Computer Science(), vol 13196. Springer, Cham. https://doi.org/10.1007/978-3-031-08421-8_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-08421-8_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08420-1

  • Online ISBN: 978-3-031-08421-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation