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Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP).
An additive algorithm for solving linear programs with zero-one variables
Egon Balas · 1965
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Machine sequencing via disjunctive graphs: An implicit enumeration algorithm
Egon Balas · 1969
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V Srinivasan · 1971
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Reinhard Haupt · 1989
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Benchmarks for basic scheduling problems
E Taillard · 1993
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Tabu search
Fred Glover and Manuel Laguna · 1998
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Benchmarks for shop scheduling problems
Ebru Demirkol, Sanjay Mehta, and Reha Uzsoy · 1998
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Deterministic job-shop scheduling: Past, present and future
AS Jain and S Meeran · 1999
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Approximation algorithms for flexible job shop problems
Klaus Jansen, Monaldo Mastrolilli, and Roberto Solis-Oba · 2000
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The disjunctive graph machine representation of the job shop scheduling problem
Jacek Błażewicz, Erwin Pesch, and Małgorzata Sterna · 2000
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Scheduling complex job shops using disjunctive graphs: a cycle elimination procedure
Scott J Mason and Kasin Oey · 2003
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Machine scheduling problems: classification, complexity and computations
AHG Rinnooy Kan · 2012
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A comparison of priority rules for the job shop scheduling problem under different flow time-and tardiness-related objective functions
Veronique Sels, Nele Gheysen, and Mario Vanhoucke · 2012
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Production planning and control for semiconductor wafer fabrication facilities: modeling, analysis, and systems
Lars Mönch, John W Fowler, and Scott J Mason · 2012
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Nonpermutation flow line scheduling by ant colony optimization
Andrea Rossi and Michele Lanzetta · 2013
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Resource management with deep reinforcement learning
Hongzi Mao, Mohammad Alizadeh, Ishai Menache, and Srikanth Kandula · 2016
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Learning combinatorial optimization algorithms over graphs
Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Reinforcement learning for solving the vehicle routing problem
Mohammadreza Nazari, Afshin Oroojlooy, Lawrence Snyder, and Martin Takác · 2018
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Learning to solve circuit-sat: An unsupervised differentiable approach
Saeed Amizadeh, Sergiy Matusevych, and Markus Weimer · 2019
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Learning local search heuristics for boolean satisfiability
Emre Yolcu and Barnabas Poczos · 2019
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Learning to perform local rewriting for combinatorial optimization
Xinyun Chen and Yuandong Tian · 2019
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Manufacturing dispatching using reinforcement and transfer learning
Shuai Zheng, Chetan Gupta, and Susumu Serita · 2019
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Learning scheduling algorithms for data processing clusters
Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, and Mohammad Alizadeh · 2019
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Smart manufacturing scheduling with edge computing using multiclass deep q network
Chun-Cheng Lin, Der-Jiunn Deng, Yen-Ling Chih, and Hsin-Ting Chiu · 2019
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Zhuwen Li, Qifeng Chen, and Vladlen Koltun · 2018
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Discovering dispatching rules from data using imitation learning: A case study for the job-shop problem
Helga Ingimundardottir and Thomas Philip Runarsson · 2018
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Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
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Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Industry 4.0–a glimpse
Saurabh Vaidya, Prashant Ambad, and Santosh Bhosle · 2018
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Smart manufacturing systems for industry 4.0: Conceptual framework, scenarios, and future perspectives
Pai Zheng, Zhiqian Sang, Ray Y Zhong, Yongkui Liu, Chao Liu, Khamdi Mubarok, Shiqiang Yu, Xun Xu, et al · 2018
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Or-tools, 2019
Laurent Perron and Vincent Furnon · 2019
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Machine learning for combinatorial optimization: a methodological tour d’horizon
Yoshua Bengio, Andrea Lodi, and Antoine Prouvost · 2020
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A learning-based iterative method for solving vehicle routing problems
Shuang Yang Hao Lu, Xingwen Zhang · 2020
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Deep reinforcement learning for traveling salesman problem with time windows and rejections
R. Zhang, A. Prokhorchuk, and J. Dauwels · 2020
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Deepweave: Accelerating job completion time with deep reinforcement learning-based coflow scheduling
Penghao Sun, Zehua Guo, Junchao Wang, Junfei Li, Julong Lan, and Yuxiang Hu · 2020
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Learning scheduling policies for multi-robot coordination with graph attention networks
Z. Wang and M. Gombolay · 2020
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Temporal multi-graph convolutional network for traffic flow prediction
Mingqi Lv, Zhaoxiong Hong, Ling Chen, Tieming Chen, Tiantian Zhu, and Shouling Ji · 2020
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A comprehensive survey on graph neural networks
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu · 2020
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A gentle introduction to deep learning for graphs
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