Document Open Access Logo

Assembly Line Preliminary Design Optimization for an Aircraft

Authors Stéphanie Roussel , Thomas Polacsek , Anouck Chan

PDF
Thumbnail PDF

File

LIPIcs.CP.2023.32.pdf
  • Filesize: 1.71 MB
  • 19 pages

Document Identifiers

Author Details

Stéphanie Roussel
  • ONERA, ONERA DTIS, Toulouse, Université de Toulouse, France
Thomas Polacsek
  • ONERA, ONERA DTIS, Toulouse, Université de Toulouse, France
Anouck Chan
  • ONERA, ONERA DTIS, Toulouse, Université de Toulouse, France

Cite As Get BibTex

Stéphanie Roussel, Thomas Polacsek, and Anouck Chan. Assembly Line Preliminary Design Optimization for an Aircraft. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 32:1-32:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.CP.2023.32

Abstract

In the aeronautics industry, each aircraft family has a dedicated manufacturing system. This system is classically designed once the aircraft design is completely finished, which might lead to poor performance. To mitigate this issue, a strategy is to take into account the production system as early as possible in the aircraft design process. In this work, we define the Assembly Line Preliminary Design Problem, which consists in defining, for a given aircraft design, the best assembly line layout and the type and number of machines equipping each workstation. We propose a Constraint Programming encoding for that problem, along with an algorithm based on epsilon constraint for exploring the set of Pareto solutions. We present experiments run on a set of real industrial data. The results show that the approach is promising and offers support to experts in order to compare aircraft designs with each other.

Subject Classification

ACM Subject Classification
  • Applied computing → Computer-aided manufacturing
Keywords
  • Assembly line design
  • Constraint Programming
  • Multi-objective
  • Industry 4.0

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Supplementary Materials

References

  1. Hacı Mehmet Alakaş. General resource-constrained assembly line balancing problem: conjunction normal form based constraint programming models. Soft Computing, 25(8):6101-6111, 2021. Google Scholar
  2. Hacı Mehmet Alakaş, Mehmet Pınarbaşı, and Mustafa Yüzükırmızı. Constraint programming model for resource-constrained assembly line balancing problem. Soft Computing, 24:5367-5375, 2020. Google Scholar
  3. Dmitry Arkhipov, Olga Battaïa, Julien Cegarra, and Alexander Lazarev. Operator assignment problem in aircraft assembly lines: a new planning approach taking into account economic and ergonomic constraints. Procedia CIRP, 76:63-66, 2018. Google Scholar
  4. Francisco Ballestín and Rosa Blanco. Theoretical and practical fundamentals for multi-objective optimisation in resource-constrained project scheduling problems. Computers & Operations Research, 38(1):51-62, 2011. Project Management and Scheduling. URL: https://doi.org/10.1016/j.cor.2010.02.004.
  5. Olga Battaïa and Alexandre Dolgui. A taxonomy of line balancing problems and their solution approaches. International Journal of Production Economics, 142(2):259-277, 2013. Anticipation of risks impacts and industrial performance evaluation in distributed organizations life cycles. URL: https://doi.org/10.1016/j.ijpe.2012.10.020.
  6. Alexander Biele and Lars Mönch. Hybrid approaches to optimize mixed-model assembly lines in low-volume manufacturing. Journal of Heuristics, 24(1):49-81, 2018. Google Scholar
  7. Tamara Borreguero, Alvaro García, and Miguel Ortega. Scheduling in the aeronautical industry using a mixed integer linear problem formulation. Procedia engineering, 132:982-989, 2015. Google Scholar
  8. Nils Boysen, Philipp Schulze, and Armin Scholl. Assembly line balancing: What happened in the last fifteen years? European Journal of Operational Research, 301(3):797-814, 2022. URL: https://doi.org/10.1016/j.ejor.2021.11.043.
  9. Peter Brucker, Andreas Drexl, Rolf Möhring, Klaus Neumann, and Erwin Pesch. Resource-constrained project scheduling: Notation, classification, models, and methods. European journal of operational research, 112(1):3-41, 1999. Google Scholar
  10. Jens Buergin, Sina Helming, Jan Andreas, Philippe Blaettchen, Yannick Schweizer, Frank Bitte, Benjamin Haefner, and Gisela Lanza. Local order scheduling for mixed-model assembly lines in the aircraft manufacturing industry. Production Engineering, 12:759-767, 2018. Google Scholar
  11. Yossi Bukchin and Tal Raviv. Constraint programming for solving various assembly line balancing problems. Omega, 78:57-68, 2018. URL: https://doi.org/10.1016/j.omega.2017.06.008.
  12. Anouck Chan, Anthony Fernandes Pires, Thomas Polacsek, and Stéphanie Roussel. The aircraft and its manufacturing system: From early requirements to global design. In Xavier Franch, Geert Poels, Frederik Gailly, and Monique Snoeck, editors, Advanced Information Systems Engineering - 34th International Conference, CAiSE 2022, Leuven, Belgium, June 6-10, 2022, Proceedings, volume 13295 of Lecture Notes in Computer Science, pages 164-179. Springer, 2022. URL: https://doi.org/10.1007/978-3-031-07472-1_10.
  13. Hicham Chehade, Alexandre Dolgui, Frédéric Dugardin, Lina Makdessian, and Farouk Yalaoui. Multi-objective approach for production line equipment selection. Management and Production Engineering Review, 3(1):4-17, 2012. Google Scholar
  14. G. Heike, M. Ramulu, E. Sorenson, P Shanahan, and Kamran Moinzadeh. Mixed model assembly alternatives for low-volume manufacturing: the case of the aerospace industry. International Journal of Production Economics, 72(2):103-120, 2001. Google Scholar
  15. Damla Kizilay and Zeynel Abidin Çil. Constraint programming approach for multi-objective two-sided assembly line balancing problem with multi-operator stations. Engineering Optimization, 53(8):1315-1330, 2021. URL: https://doi.org/10.1080/0305215X.2020.1786081.
  16. Philippe Laborie, Jérôme Rogerie, Paul Shaw, and Petr Vilím. IBM ILOG CP optimizer for scheduling: 20+ years of scheduling with constraints at IBM/ILOG. Constraints, 23:210-250, 2018. Google Scholar
  17. Christophe Lecoutre and Nicolas Szczepanski. PyCSP3: Modeling Combinatorial Constrained Problems in Python. Technical report, arXiv, December 2021. URL: https://hal-univ-artois.archives-ouvertes.fr/hal-03701203.
  18. Fernando Mas, José Luis Menéndez, Manuel Oliva, Javier Servan, Rebeca Arista, and Carmelo Del Valle. Design within complex environments: Collaborative engineering in the aerospace industry. In Information System Development: Improving Enterprise Communication, pages 197-205. Springer, 2014. Google Scholar
  19. Jonathan Oesterle and Lionel Amodeo. Efficient multi-objective optimization method for the mixed-model-line assembly line design problem. Procedia CIRP, 17:82-87, 2014. Variety Management in Manufacturing. URL: https://doi.org/10.1016/j.procir.2014.01.038.
  20. Jonathan Oesterle, Lionel Amodeo, and Farouk Yalaoui. A comparative study of multi-objective algorithms for the assembly line balancing and equipment selection problem under consideration of product design alternatives. Journal of intelligent Manufacturing, 30:1021-1046, 2019. Google Scholar
  21. Tenda Okimoto, Yongjoon Joe, Atsushi Iwasaki, Toshihiro Matsui, Katsutoshi Hirayama, and Makoto Yokoo. Interactive algorithm for multi-objective constraint optimization. In Michela Milano, editor, Principles and Practice of Constraint Programming, pages 561-576, Berlin, Heidelberg, 2012. Springer Berlin Heidelberg. Google Scholar
  22. Thomas Polacsek, Stéphanie Roussel, Cédric Pralet, and Claude Cuiller. Design for efficient production, A model-based approach. In Manuel Kolp, Jean Vanderdonckt, Monique Snoeck, and Yves Wautelet, editors, 13th International Conference on Research Challenges in Information Science, RCIS 2019, Brussels, Belgium, May 29-31, 2019, pages 1-6. IEEE, 2019. URL: https://doi.org/10.1109/RCIS.2019.8877088.
  23. Cédric Pralet, Stéphanie Roussel, Thomas Polacsek, François Bouissière, Claude Cuiller, Pierre-Eric Dereux, Stéphane Kersuzan, and Marc Lelay. A scheduling tool for bridging the gap between aircraft design and aircraft manufacturing. Proceedings of the International Conference on Automated Planning and Scheduling, 28(1):347-355, June 2018. URL: https://doi.org/10.1609/icaps.v28i1.13910.
  24. Brahim Rekiek, Alain Delchambre, Alexandre Dolgui, and Antoneta Bratcu. Assembly line design: A survey. IFAC Proceedings Volumes, 35(1):155-166, 2002. 15th IFAC World Congress. URL: https://doi.org/10.3182/20020721-6-ES-1901.01647.
  25. Emma Rollon and Javier Larrosa. Multi-objective propagation in constraint programming. Frontiers in Artificial Intelligence and Applications, 141:128, 2006. Google Scholar
  26. Tamara Borreguero Sanchidrián, Tom Portoleau, Christian Artigues, Alvaro García Sánchez, Miguel Ortega Mier, and Pierre Lopez. Exact and heuristic methods for an aeronautical assembly line time-constrained scheduling problem with multiple modes and a resource leveling objective. hal-03344445, 2021. Google Scholar
  27. Nicolas Schwind and Demirović Emir. Representative solutions for bi-objective optimisation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 1436-1443, 2020. Google Scholar
  28. Pascal Van Hentenryck, Laurent Michel, Laurent Perron, and Jean-Charles Régin. Constraint programming in opl. In PPDP, volume 99, pages 98-116. Springer, 1999. Google Scholar
  29. Yeo Keun Kim, Yong Ju Kim, and Yeongho Kim. Genetic algorithms for assembly line balancing with various objectives. Computers & Industrial Engineering, 30(3):397-409, 1996. IE in Korea. URL: https://doi.org/10.1016/0360-8352(96)00009-5.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact