A learning large neighborhood search for the staff rerostering problem

DOI10.1007/978-3-031-08011-1_20zbMath1502.90103OpenAlexW4285247237MaRDI QIDQ2170207

Fabio F. Oberweger, Günther R. Raidl, Marc Huber, Elina Rönnberg

Publication date: 30 August 2022

Full work available at URL: https://doi.org/10.1007/978-3-031-08011-1_20

zbMATH Keywords

machine learninglarge neighborhood searchimitation learningstaff rerostering

Mathematics Subject Classification ID

Mixed integer programming (90C11) Learning and adaptive systems in artificial intelligence (68T05) Theory of organizations, manpower planning in operations research (90B70)

Uses Software

• Adam
• AlphaGo
• SeaPearl

Cites Work

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• Solving the problem of rerostering nurse schedules with hard constraints: new multicommodity flow models
• An evolutionary approach for the nurse rerostering problem
• Solving a bi-objective nurse rerostering problem by using a utopic Pareto genetic heuristic
• Staff scheduling and rostering: a review of applications, methods and models.
• Local branching
• The nurse rerostering problem: strategies for reconstructing disrupted schedules
• An integer multicommodity flow model applied to the rerostering of nurse schedules
• SeaPearl: a constraint programming solver guided by reinforcement learning
• Personnel scheduling: a literature review
• A genetic algorithm approach to a nurse rerostering problem
• Distributing battery swapping stations for electric scooters in an urban area

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