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Aircraft Resource-Constrained Assembly Line Balancing with Learning Effect: A Constraint Programming Approach

Authors Duc Anh Le , Stéphanie Roussel , Christophe Lecoutre

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Subject Classification

ACM Subject Classification
  • Applied computing → Decision analysis
Keywords
  • Assembly Line Balancing
  • Resource-Constrained
  • Learning Effect
  • Ramp-Up
  • Aeronautic
  • Constraint Programming
  • Dominance-Breaking

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Abstract

Balancing aeronautical assembly lines is a major challenge in modern aerospace manufacturing. Aircraft manufacturing plants typically have a predetermined production rate, but the production system requires a period of adaptation at start-up. This phenomenon, known as the learning effect, refers to the gradual improvement in efficiency through task repetition, thereby reducing task duration. However, the stability of an assembly line is also a critical factor, as any change in the production process incurs costs. In this study, Constraint Programming (CP) is used to optimise assembly line balancing, taking into account the learning effect to address the trade-off between achieving target production rates and minimising adjustments to the line.

Cite As Get BibTex

Duc Anh Le, Stéphanie Roussel, and Christophe Lecoutre. Aircraft Resource-Constrained Assembly Line Balancing with Learning Effect: A Constraint Programming Approach. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 25:1-25:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.CP.2025.25

Author Details

Duc Anh Le
  • DTIS, ONERA, Université de Toulouse, France
Stéphanie Roussel
  • DTIS, ONERA, Université de Toulouse, France
Christophe Lecoutre
  • CRIL, Université d'Artois & CNRS, France

Funding

  • Roussel, Stéphanie: This work has benefitted from the AI Interdisciplinary Institute ANITI. ANITI is funded by the French "Investing for the Future – PIA3" program under the Grant agreement n°ANR-23-IACL-0002.
  • Lecoutre, Christophe: This work has benefited from the support of the National Research Agency under France 2030, MAIA Project ANR-22-EXES-0009.

Supplementary Materials

References

  1. Z.Z. El Abidine and T. Koltai. The effect of learning on assembly line balancing: A review. Periodica Polytechnica Social and Management Sciences, 2023. Google Scholar
  2. K. Ağpak and H. Gökçen. Assembly line balancing: Two resource constrained cases. International Journal of Production Economics, 96(1):129-140, 2005. Google Scholar
  3. H.M. Alakaş, M. Pınarbaşı, and M. Yüzükırmızı. Constraint programming model for resource-constrained assembly line balancing problem. Soft Computing, 24:5367-5375, 2020. URL: https://doi.org/10.1007/S00500-019-04294-8.
  4. E. Alhomaidhi. Enhancing efficiency and adaptability in mixed model line balancing through the fusion of learning effects and worker prerequisites. International Journal of Industrial Engineering Computations, 15(2):541-552, 2024. Google Scholar
  5. C. Artigues, S. Hartmann, and M. Vanhoucke. Fifty years of research on resource-constrained project scheduling explored from different perspectives. European Journal of Operational Research, 2025. Google Scholar
  6. Z. Bao and L. Chen. A matheuristic approach for the aircraft final assembly line balancing problem considering learning curve. In 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pages 1389-1393. IEEE, 2023. URL: https://doi.org/10.1109/IEEM58616.2023.10407009.
  7. O. Battaïa and A. Dolgui. A taxonomy of line balancing problems and their solution approaches. International Journal of Production Economics, 142(2):259-277, 2013. Google Scholar
  8. O. Battaïa and A. Dolgui. Hybridizations in line balancing problems: A comprehensive review on new trends and formulations. International Journal of Production Economics, 250:108673, 2022. Google Scholar
  9. D. Biskup. A state-of-the-art review on scheduling with learning effects. European Journal of Operational Research, 188(2):315-329, 2008. URL: https://doi.org/10.1016/J.EJOR.2007.05.040.
  10. N. Boysen, P. Schulze, and A. 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.
  11. J. Bukchin and M. Masin. Multi-objective design of team oriented assembly systems. European Journal of Operational Research, 156(2):326-352, 2004. URL: https://doi.org/10.1016/S0377-2217(03)00054-7.
  12. R. Bultó, E. Viles, and R. Mateo. Overview of ramp-up curves: A literature review and new challenges. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 232(5):755-765, 2018. Google Scholar
  13. G. Chu and P. Stuckey. Dominance breaking constraints. Constraints, 20:155-182, 2015. URL: https://doi.org/10.1007/S10601-014-9173-7.
  14. Z.A. Çil and D. Kizilay. Constraint programming model for multi-manned assembly line balancing problem. Computers & Operations Research, 124:105069, 2020. URL: https://doi.org/10.1016/J.COR.2020.105069.
  15. Y. Cohen, G. Vitner, and S.C. Sarin. Optimal allocation of work in assembly lines for lots with homogenous learning. European Journal of Operational Research, 168(3):922-931, 2006. Balancing Assembly and Transfer lines. URL: https://doi.org/10.1016/J.EJOR.2004.07.037.
  16. A. Corominas, L. Ferrer, and R. Pastor. Assembly line balancing: general resource-constrained case. International Journal of Production Research, 49(12):3527-3542, 2011. Google Scholar
  17. S.G. Dimitriadis. Assembly line balancing and group working: A heuristic procedure for workers’ groups operating on the same product and workstation. Computers & Operations Research, 33(9):2757-2774, 2006. Part Special Issue: Anniversary Focused Issue of Computers & Operations Research on Tabu Search. URL: https://doi.org/10.1016/J.COR.2005.02.027.
  18. E.B. Edis. Constraint programming approaches to disassembly line balancing problem with sequencing decisions. Computers & Operations Research, 126:105111, 2021. URL: https://doi.org/10.1016/J.COR.2020.105111.
  19. E.H. Grosse, C.H. Glock, and S. Müller. Production economics and the learning curve: A meta-analysis. International Journal of Production Economics, 170:401-412, 2015. Google Scholar
  20. W.B. Helgeson and D.P. Birnie. Assembly line balancing using the ranked positional weight technique. Journal of industrial engineering, 12(6):394-398, 1961. Google Scholar
  21. J. Heraud, K. Medini, and A.L. Andersen. Managing agile ramp-up projects in manufacturing-status quo and recommendations. CIRP Journal of Manufacturing Science and Technology, 45:125-137, 2023. Google Scholar
  22. A. Hill, J. Ticktin, and T.W.M. Vossen. A computational study of constraint programming approaches for resource-constrained project scheduling with autonomous learning effects. In Integration of Constraint Programming, Artificial Intelligence, and Operations Research: 18th International Conference, CPAIOR 2021, Vienna, Austria, July 5-8, 2021, Proceedings 18, pages 26-44. Springer, 2021. URL: https://doi.org/10.1007/978-3-030-78230-6_2.
  23. T. Koltai and N. Kalló. Analysis of the effect of learning on the throughput-time in simple assembly lines. Computers & Industrial Engineering, 111:507-515, 2017. URL: https://doi.org/10.1016/J.CIE.2017.03.034.
  24. P. Laborie, J. Rogerie, P. Shaw, and P. Vilím. IBM ILOG CP optimizer for scheduling: 20+ years of scheduling with constraints at IBM/ILOG. Constraints, 23:210-250, 2018. URL: https://doi.org/10.1007/S10601-018-9281-X.
  25. D.A. Le, S. Roussel, and C. Lecoutre. Dataset for the assembly line balancing problem with learning effect, 2025. URL: https://doi.org/10.57745/EBDN5W.
  26. D.A. Le, S. Roussel, C. Lecoutre, and A. Chan. Learning Effect and Compound Activities in High Multiplicity RCPSP: Application to Satellite Production. In 30th International Conference CP, 2024. URL: https://doi.org/10.4230/LIPIcs.CP.2024.18.
  27. Y. Li and T.O. Boucher. Assembly line balancing problem with task learning and dynamic task reassignment. The International Journal of Advanced Manufacturing Technology, 88:3089-3097, 2017. Google Scholar
  28. Y. Li, D. Liu, and I. Kucukkoc. Mixed-model assembly line balancing problem considering learning effect and uncertain demand. Journal of Computational and Applied Mathematics, 422:114823, 2023. URL: https://doi.org/10.1016/J.CAM.2022.114823.
  29. D. Ogan and M. Azizoglu. A branch and bound method for the line balancing problem in u-shaped assembly lines with equipment requirements. Journal of Manufacturing Systems, 36:46-54, 2015. Google Scholar
  30. C. Otto and A. Otto. Extending assembly line balancing problem by incorporating learning effects. International Journal of Production Research, 52(24):7193-7208, 2014. Google Scholar
  31. V. Van Peteghem and M. Vanhoucke. A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem. European Journal of Operational Research, 201(2):409-418, 2010. URL: https://doi.org/10.1016/J.EJOR.2009.03.034.
  32. V. Van Peteghem and M. Vanhoucke. Influence of learning in resource-constrained project scheduling. Computers & Industrial Engineering, 87:569-579, 2015. URL: https://doi.org/10.1016/J.CIE.2015.06.007.
  33. X. Pucel and S. Roussel. Constraint Programming Model for Assembly Line Balancing and Scheduling with Walking Workers and Parallel Stations. In Paul Shaw, editor, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024), volume 307, pages 23:1-23:21, 2024. URL: https://doi.org/10.4230/LIPICS.CP.2024.23.
  34. S. Roussel, T. Polacsek, and A. Chan. Assembly line preliminary design optimization for an aircraft. In CP 2023 (The 29th International Conference on Principles and Practice of Constraint Programming), 2023. URL: https://doi.org/10.4230/LIPIcs.CP.2023.32.
  35. K.E. Stecke and M. Mokhtarzadeh. Balancing collaborative human–robot assembly lines to optimise cycle time and ergonomic risk. International Journal of Production Research, 60(1):25-47, 2022. URL: https://doi.org/10.1080/00207543.2021.1989077.
  36. P. van Hentenryck. The OPL Optimization Programming Language. The MIT Press, 1999. Google Scholar
  37. J.M. Wilson. Formulation of a problem involving assembly lines with multiple manning of work stations. International Journal of Production Research, 24(1):59-63, 1986. Google Scholar
  38. T.P. Wright. Factors affecting the cost of airplanes. Journal of the aeronautical sciences, 3(4):122-128, 1936. Google Scholar
  39. L.E. Yelle. The learning curve: Historical review and comprehensive survey. Decision sciences, 10(2):302-328, 1979. Google Scholar
  40. Z.A. ÇİL. An exact solution method for multi-manned disassembly line design with and/or precedence relations. Applied Mathematical Modelling, 99:785-803, 2021. URL: https://doi.org/10.1016/j.apm.2021.07.013.
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