Learning to reduce state-expanded networks for multi-activity shift scheduling

DOI10.1007/978-3-030-78230-6_24OpenAlexW3167021915MaRDI QIDQ2117239

Till Porrmann, Michael Römer

Publication date: 21 March 2022

Full work available at URL: https://doi.org/10.1007/978-3-030-78230-6_24

Mathematics Subject Classification ID

Combinatorial optimization (90C27) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Uses Software

• XGBoost

Cites Work

• Unnamed Item
• A large neighbourhood search approach to the multi-activity shift scheduling problem
• An implicit model for multi-activity shift scheduling problems
• A matheuristic based on Lagrangian relaxation for the multi-activity shift scheduling problem
• Machine learning for combinatorial optimization: a methodological tour d'horizon
• Formal languages for integer programming modeling of shift scheduling problems
• Grammar-Based Integer Programming Models for Multiactivity Shift Scheduling
• Constraint Programming Based Column Generation for Employee Timetabling

This page was built for publication: Learning to reduce state-expanded networks for multi-activity shift scheduling