ML Anthology

Proactive and Reactive Constraint Programming for Stochastic Project Scheduling with Maximal Time-Lags

Kim van den Houten, Léon Planken, Esteban Freydell, David M. J. Tax, Mathijs de Weerdt
AAAI 2025 pp. 26534-26541
doi:10.1609/AAAI.V39I25.34854 /aaai/2025/vandenhouten2025aaai-proactive/

Abstract

This study investigates scheduling strategies for the stochastic resource-constrained project scheduling problem with maximal time lags (SRCPSP/max). Recent advances in Constraint Programming (CP) and Temporal Networks have re-invoked interest in evaluating the advantages and drawbacks of various proactive and reactive scheduling methods. First, we present a new, CP-based fully proactive method. Second, we show how a reactive approach can be constructed using an online rescheduling procedure. A third contribution is based on partial order schedules and uses Simple Temporal Networks with Uncertainty (STNUs). Our statistical analysis shows that the STNU-based algorithm performs best in terms of solution quality, while also showing good relative offline and online computation time

PDF AAAI Semantic Scholar

Cite

Text

van den Houten et al. "Proactive and Reactive Constraint Programming for Stochastic Project Scheduling with Maximal Time-Lags." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I25.34854

Markdown

[van den Houten et al. "Proactive and Reactive Constraint Programming for Stochastic Project Scheduling with Maximal Time-Lags." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/vandenhouten2025aaai-proactive/) doi:10.1609/AAAI.V39I25.34854

BibTeX

@inproceedings{vandenhouten2025aaai-proactive,
  title     = {{Proactive and Reactive Constraint Programming for Stochastic Project Scheduling with Maximal Time-Lags}},
  author    = {van den Houten, Kim and Planken, Léon and Freydell, Esteban and Tax, David M. J. and de Weerdt, Mathijs},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {26534-26541},
  doi       = {10.1609/AAAI.V39I25.34854},
  url       = {https://mlanthology.org/aaai/2025/vandenhouten2025aaai-proactive/}
}