Pattern mining‐based pruning strategies in stochastic local searches for scheduling problems
From MaRDI portal
Publication:6082181
DOI10.1111/ITOR.12984OpenAlexW3165191648MaRDI QIDQ6082181
Unnamed Author, Unnamed Author, Unnamed Author, Unnamed Author
Publication date: 29 November 2023
Published in: International Transactions in Operational Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/itor.12984
machine learningjob-shopscheduling problemstochastic local searchpattern miningresource constrained project scheduling problem
Related Items (1)
Cites Work
- Unnamed Item
- Synergies of operations research and data mining
- The job shop scheduling problem: Conventional and new solution techniques
- PSPLIB -- a project scheduling problem library
- A machine-learning based memetic algorithm for the multi-objective permutation flowshop scheduling problem
- A generalized permutation approach to job shop scheduling with genetic algorithms
- Synergies between operations research and data mining: the emerging use of multi-objective approaches
- Unconventional initialization methods for differential evolution
- A neighborhood for complex job shop scheduling problems with regular objectives
- Learning variable neighborhood search for a scheduling problem with time windows and rejections
- Operations research and data mining
- A review of machine learning in dynamic scheduling of flexible manufacturing systems
- Job shop scheduling with a genetic algorithm and machine learning
- A learning-based methodology for dynamic scheduling in distributed manufacturing systems
- Combining metaheuristics with mathematical programming, constraint programming and machine learning
This page was built for publication: Pattern mining‐based pruning strategies in stochastic local searches for scheduling problems
