Non‐greedy heuristics and augmented neural networks for the open‐shop scheduling problem

DOI10.1002/nav.20102zbMath1151.90400OpenAlexW1963588684WikidataQ125626978 ScholiaQ125626978MaRDI QIDQ5438534

Anurag Agarwal, Selcuk Colak

Publication date: 23 January 2008

Published in: Naval Research Logistics (NRL) (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/nav.20102

zbMATH Keywords

heuristicsschedulingneural networksopen shop

Mathematics Subject Classification ID

Deterministic scheduling theory in operations research (90B35) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (8)

New efficient heuristics for scheduling open shops with makespan minimization ⋮ A hybrid neural network approach to minimize total completion time on a single batch processing machine ⋮ Four decades of research on the open-shop scheduling problem to minimize the makespan ⋮ A new particle swarm optimization for the open shop scheduling problem ⋮ NeuroGenetic approach for combinatorial optimization: an exploratory analysis ⋮ Theoretical insights into the augmented-neural-network approach for combinatorial optimization ⋮ Dynamic programming approach for solving the open shop problem ⋮ Performance analysis of rotation schedule and improved strategy for open shop problem to minimise makespan

This page was built for publication: Non‐greedy heuristics and augmented neural networks for the open‐shop scheduling problem