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Constraint Logic Programming for Real-World Test Laboratory Scheduling

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Titel: Constraint Logic Programming for Real-World Test Laboratory Scheduling
Autoren: Tobias Geibinger, Florian Mischek, Nysret Musliu
Quelle: Proceedings of the AAAI Conference on Artificial Intelligence. 35:6358-6366
Verlagsinformationen: Association for the Advancement of Artificial Intelligence (AAAI), 2021.
Publikationsjahr: 2021
Schlagwörter: 0211 other engineering and technologies, 02 engineering and technology
Beschreibung: The Test Laboratory Scheduling Problem (TLSP) and its subproblem TLSP-S are real-world industrial scheduling problems that are extensions of the Resource-Constrained Project Scheduling Problem (RCPSP). Besides several additional constraints, TLSP includes a grouping phase where the jobs to be scheduled have to be assembled from smaller tasks and derive their properties from this grouping. For TLSP-S such a grouping is already part of the input. In this work, we show how TLSP-S can be solved by Answer-set Programming extended with ideas from other constraint solving paradigms. We propose a novel and efficient encoding and apply an answer-set solver for constraint logic programs called clingcon. Additionally, we utilize our encoding in a Very Large Neighborhood Search framework and compare our methods with the state of the art approaches. Our approach provides new upper bounds and optimality proofs for several existing benchmark instances in the literature.
Publikationsart: Article
ISSN: 2374-3468
2159-5399
DOI: 10.1609/aaai.v35i7.16789
Dokumentencode: edsair.doi...........2893a5a9d49c40f4fe630470ab72012f
Datenbank: OpenAIRE
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  Data: Constraint Logic Programming for Real-World Test Laboratory Scheduling
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  Data: The Test Laboratory Scheduling Problem (TLSP) and its subproblem TLSP-S are real-world industrial scheduling problems that are extensions of the Resource-Constrained Project Scheduling Problem (RCPSP). Besides several additional constraints, TLSP includes a grouping phase where the jobs to be scheduled have to be assembled from smaller tasks and derive their properties from this grouping. For TLSP-S such a grouping is already part of the input. In this work, we show how TLSP-S can be solved by Answer-set Programming extended with ideas from other constraint solving paradigms. We propose a novel and efficient encoding and apply an answer-set solver for constraint logic programs called clingcon. Additionally, we utilize our encoding in a Very Large Neighborhood Search framework and compare our methods with the state of the art approaches. Our approach provides new upper bounds and optimality proofs for several existing benchmark instances in the literature.
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