Document Open Access Logo

Learning Effect and Compound Activities in High Multiplicity RCPSP: Application to Satellite Production

Authors Duc Anh Le , Stéphanie Roussel , Christophe Lecoutre , Anouck Chan

PDF
Thumbnail PDF

File

LIPIcs.CP.2024.18.pdf
  • Filesize: 0.94 MB
  • 25 pages

Document Identifiers

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
Anouck Chan
  • DTIS, ONERA, Université de Toulouse, France

Funding

Stéphanie Roussel, Anouck Chan: This work has been partly realised with the support of the French government, in the context of the BPI PSPC project "LiChIE" of the "Programme d’Investissements d’Avenir".
  • Lecoutre, Christophe: This work has benefited from the support of the National Research Agency under France 2030, MAIA Project ANR-22-EXES-0009.

Cite As Get BibTex

Duc Anh Le, Stéphanie Roussel, Christophe Lecoutre, and Anouck Chan. Learning Effect and Compound Activities in High Multiplicity RCPSP: Application to Satellite Production. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 18:1-18:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.CP.2024.18

Abstract

This paper addresses the High Multiplicity Resource-Constrained Project Scheduling Problem (HM-RCPSP), in which multiple projects are performed iteratively while sharing limited resources. We extend this problem by integrating the learning effect, which makes the duration of some activities decrease when they are repeated. Learning effect can be represented by any decreasing function, allowing us to get flexibility in modeling various scenarios. Additionally, we take composition of activities into consideration for reasoning about precedence and resources in a more abstract way. A Constraint Programming model is proposed for this richer problem, including a symmetry-breaking technique applied to some activities. We also present a heuristic-based search strategy. The effectiveness of these solving approaches is evaluated through an experimentation conducted on data concerning real-world satellite assembly lines, as well as on some adapted literature benchmarks. Obtained results demonstrate that our methods serve as robust baselines for addressing this novel problem (denoted by HM-RCPSP/L-C).

Subject Classification

ACM Subject Classification
  • Applied computing → Decision analysis
Keywords
  • High-multiplicity Project Scheduling
  • Learning Effect
  • Compound Activities
  • Satellite Assembly Line
  • Constraint Programming
  • Symmetry Breaking

Metrics

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

Supplementary Materials

References

  1. J.P. Amor. Scheduling programs with repetitive projects using composite learning curve approximations. Project Management Journal, 33(3):16-29, 2002. Google Scholar
  2. C. Artigues, S. Demassey, and E. Neron. Resource-Constrained Project Scheduling: Models, Algorithms, Extensions and Applications. ISTE/Wiley, 2008. URL: https://hal.science/hal-00482946.
  3. A. Bachman and A. Janiak. Scheduling jobs with position-dependent processing times. Journal of the Operational Research Society, 55(3):257-264, 2004. Google Scholar
  4. D. Biskup. Single-machine scheduling with learning considerations. European Journal of Operational Research, 115(1):173-178, 1999. Google Scholar
  5. D. Biskup. A state-of-the-art review on scheduling with learning effects. European Journal of Operational Research, 188(2):315-329, 2008. Google Scholar
  6. A. Bonfietti, M. Lombardi, L. Benini, and M. Milano. A constraint based approach to cyclic RCPSP. In Proceedings of CP'11, pages 130-144. Springer, 2011. Google Scholar
  7. C. Daehnick, I. Klinghoffer, B. Maritz, and B. Wiseman. Large LEO satellite constellations: Will it be different this time? McKinsey & Company, 4, 2020. Google Scholar
  8. S.J. Edwards, D. Baatar, K. Smith-Miles, and A.T. Ernst. Symmetry breaking of identical projects in the high-multiplicity rcpsp/max. Journal of the Operational Research Society, 72(8):1822-1843, 2021. Google Scholar
  9. M. Eugeni, T. Quercia, M. Bernabei, A. Boschetto, F. Costantino, L. Lampani, A. Marchetti Spaccamela, A. Lombardo, M. Mecella, L. Querzoni, R. Usinger, M. Aliprandi, A. Stancu, M.M. Ivagnes, G. Morabito, A. Simoni, A. Brandão, and P. Gaudenzi. An industry 4.0 approach to large scale production of satellite constellations. the case study of composite sandwich panel manufacturing. Acta Astronautica, 192:276-290, March 2022. URL: https://doi.org/10.1016/j.actaastro.2021.12.039.
  10. J.D. García-Nieves, J.L. Ponz-Tienda, A. Ospina-Alvarado, and M. Bonilla-Palacios. Multipurpose linear programming optimization model for repetitive activities scheduling in construction projects. Automation in Construction, 105:102799, 2019. URL: https://doi.org/10.1016/j.autcon.2019.03.020.
  11. 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
  12. S. Hartmann and D. Briskorn. An updated survey of variants and extensions of the resource-constrained project scheduling problem. Eur. J. Oper. Res., 297(1):1-14, 2022. URL: https://doi.org/10.1016/J.EJOR.2021.05.004.
  13. 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 Proceedings of CPAIOR'21, pages 26-44. Springer, 2021. Google Scholar
  14. D.E. Joslin and D.P. Clements. Squeaky wheel optimization. Journal of Artificial Intelligence Research, 10:353-373, 1999. Google Scholar
  15. R. Kolisch and A. Sprecher. Psplib - a project scheduling problem library: Or software - orsep operations research software exchange program. European Journal of Operational Research, 96(1):205-216, 1997. URL: https://doi.org/10.1016/S0377-2217(96)00170-1.
  16. 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. Google Scholar
  17. Y.C. Law and J. Lee. Global constraints for integer and set value precedence. In Proceedings of CP'04, pages 362-376, 2004. Google Scholar
  18. D.A. Le and S. Roussel. Dataset for the High Multiplicity RCPSP with Learning Effect and Compound Activities, 2024. Dataset, version 3.2. (visited on 2024-08-19). URL: https://doi.org/10.57745/ASGLBH.
  19. 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. Google Scholar
  20. V. Van Peteghem and M. Vanhoucke. Influence of learning in resource-constrained project scheduling. Computers & Industrial Engineering, 87:569-579, 2015. Google Scholar
  21. S. Qin, S. Liu, H. Kuang, et al. Piecewise linear model for multiskilled workforce scheduling problems considering learning effect and project quality. Mathematical problems in Engineering, 2016, 2016. Google Scholar
  22. M. Gómez Sánchez, E. Lalla-Ruiz, A. Fernández Gil, C. Castro, and S. Voß. Resource-constrained multi-project scheduling problem: A survey. European Journal of Operational Research, 309(3):958-976, 2023. URL: https://doi.org/10.1016/j.ejor.2022.09.033.
  23. T.P. Wright. Factors affecting the cost of airplanes. Journal of the aeronautical sciences, 3(4):122-128, 1936. Google Scholar
  24. M.C. Wu and S.H. Sun. A project scheduling and staff assignment model considering learning effect. The International Journal of Advanced Manufacturing Technology, 28:1190-1195, 2006. Google Scholar
  25. L.E. Yelle. The learning curve: Historical review and comprehensive survey. Decision sciences, 10(2):302-328, 1979. Google Scholar
  26. Y. Yin, D. Xu, K. Sun, and H. Li. Some scheduling problems with general position-dependent and time-dependent learning effects. Information Sciences, 179(14):2416-2425, 2009. Google Scholar
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