Proactive and Reactive Constraint Programming for Stochastic Project Scheduling with Maximal Time-Lags
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913752693080064 |
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| author | Houten, Kim van den Planken, Léon Freydell, Esteban Tax, David M. J. de Weerdt, Mathijs |
| author_facet | Houten, Kim van den Planken, Léon Freydell, Esteban Tax, David M. J. de Weerdt, Mathijs |
| contents | 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 reinvoked 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09107 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Proactive and Reactive Constraint Programming for Stochastic Project Scheduling with Maximal Time-Lags Houten, Kim van den Planken, Léon Freydell, Esteban Tax, David M. J. de Weerdt, Mathijs Artificial Intelligence 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 reinvoked 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. |
| title | Proactive and Reactive Constraint Programming for Stochastic Project Scheduling with Maximal Time-Lags |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2409.09107 |