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Automating Personnel Rostering by Learning Constraints Using Tensors
Department of Computer Science, KU Leuven, Leuven, Belgium.
Department of Computer Science, KU Leuven, Leuven, Belgium.
Department of Computer Science, KU Leuven, Leuven, Belgium.
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-6860-6303
2018 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Many problems in operations research require that constraints be specified in the model. Determining the right constraints is a hard and laborsome task. We propose an approach to automate this process using artificial intelligence and machine learning principles. So far there has been only little work on learning constraints within the operations research community. We focus on personnel rostering and scheduling problems in which there are often past schedules available and show that it is possible to automatically learn constraints from such examples. To realize this, we adapted some techniques from the constraint programming community and we have extended them in order to cope with multidimensional examples. The method uses a tensor representation of the example, which helps in capturing the dimensionality as well as the structure of the example, and applies tensor operations to find the constraints that are satisfied by the example. To evaluate the proposed algorithm, we used constraints from the Nurse Rostering Competition and generated solutions that satisfy these con-straints; these solutions were then used as examples to learn constraints. Experiments demonstrate that the proposed algorithm is capable of producing hu-man readable constraints that capture the underlying characteristics of the examples.

Place, publisher, year, edition, pages
2018.
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:oru:diva-94460OAI: oai:DiVA.org:oru-94460DiVA, id: diva2:1595764
Conference
DSO Workshop - IJCAI, Stockholm, Sweden, July 13-19, 2018
Available from: 2021-09-20 Created: 2021-09-20 Last updated: 2021-09-20Bibliographically approved

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De Raedt, Luc

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  • asciidoc
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