Predicting the optimal period for Cyclic Hoist Scheduling Problems

DOI10.1007/978-3-031-33271-5_16OpenAlexW4377249661MaRDI QIDQ6057265

Nikolaos Efthymiou, Neil Yorke-Smith

Publication date: 4 October 2023

Published in: Integration of Constraint Programming, Artificial Intelligence, and Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-031-33271-5_16

zbMATH Keywords

supervised learningconstraint programmingcombinatorial optimisationcyclic hoist scheduling

Mathematics Subject Classification ID

Combinatorial optimization (90C27) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Operations research and management science (90Bxx)

Cites Work

• An evolutionary approach for the design and scheduling of electroplating facilities
• Current trends in deterministic scheduling
• Learning heuristics for the TSP by policy gradient
• Accelerating the branch-and-price algorithm using machine learning
• A generalized classification scheme for crane scheduling with interference
• Machine learning for combinatorial optimization: a methodological tour d'horizon
• Deep policy dynamic programming for vehicle routing problems
• Deep-learning-based partial pricing in a branch-and-price algorithm for personalized crew rostering
• Optimal Cyclic Multi-Hoist Scheduling: A Mixed Integer Programming Approach
• Random forests

This page was built for publication: Predicting the optimal period for Cyclic Hoist Scheduling Problems