A hybrid neural network–genetic algorithm approach for permutation flow shop scheduling
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Publication:3163237
DOI10.1080/00207540802404364zbMath1197.90205OpenAlexW2050360670MaRDI QIDQ3163237
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Publication date: 25 October 2010
Published in: International Journal of Production Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00207540802404364
Related Items (2)
A new vision of approximate methods for the permutation flowshop to minimise makespan: state-of-the-art and computational evaluation ⋮ Learning to select operators in meta-heuristics: an integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem
Cites Work
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- Heuristics for scheduling in flowshop with multiple objectives
- Benchmarks for basic scheduling problems
- Optimal two- and three-stage production schedules with setup times included
- An Evaluation of Flow Shop Sequencing Heuristics
- The Complexity of Flowshop and Jobshop Scheduling
- Recognition of control chart concurrent patterns using a neural network approach
- Flowshop-scheduling problems with makespan criterion: a review
- A Heuristic Algorithm for the n Job, m Machine Sequencing Problem
- A Functional Heuristic Algorithm for the Flowshop Scheduling Problem
- Sequencing jobs on a single machine: A neural network approach
- Competitive neural network to solve scheduling problems
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