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This work proposes a self-supervised training strategy designed for combinatorial problems.
Machine sequencing via disjunctive graphs: an implicit enumeration algorithm
Egon Balas · 1969
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Resource constrained project scheduling: an experimental investigation of heuristic scheduling techniques (Supplement)
Lawrence Stephen · 1984
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The Shifting Bottleneck Procedure for Job Shop Scheduling
Joseph Adams, Egon Balas, and Daniel Zawack · 1988
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Reinhard Haupt · 1989
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Gerhard Reinelt · 1991
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Eric Taillard · 1993
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Ebru Demirkol, Sanjay Mehta, and Reha Uzsoy · 1998
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A tutorial survey of job-shop scheduling problems using genetic algorithms, part II: hybrid genetic search strategies
Runwei Cheng, Mitsuo Gen, and Yasuhiro Tsujimura · 1999
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Metaheuristics in combinatorial optimization: Overview and conceptual comparison
Christian Blum and Andrea Roli · 2003
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Pieter-Tjerk De Boer, Dirk P Kroese, Shie Mannor, and Reuven Y Rubinstein · 2005
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An Advanced Tabu Search Algorithm for the Job Shop Problem
Eugeniusz Nowicki and Czeslaw Smutnicki · 2005
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Kuo-Ling Huang and Ching-Jong Liao · 2008
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Scheduling: Theory, Algorithms, and Systems
M.L. Pinedo · 2012
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The Cross-Entropy Method for Optimization
Zdravko I. Botev, Dirk P. Kroese, Reuven Y. Rubinstein, and Pierre L’Ecuyer · 2013
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Mixed Integer Programming models for job shop scheduling: A computational analysis
Wen-Yang Ku and J. Christopher Beck · 2016
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Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Deep Reinforcement Learning That Matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
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Learning to dispatch for job shop scheduling via deep reinforcement learning
Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, and Xu Chi · 2020
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Machine learning for combinatorial optimization: A methodological tour d’horizon
Yoshua Bengio, Andrea Lodi, and Antoine Prouvost · 2021
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Combinatorial Optimization and Reasoning with Graph Neural Networks
Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, and Petar Veličković · 2021
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Reinforcement learning for combinatorial optimization: A survey
Nina Mazyavkina, Sergey Sviridov, Sergei Ivanov, and Evgeny Burnaev · 2021
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Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning
Junyoung Park, Jaehyeong Chun, Sang Kim, Youngkook Kim, and Jinkyoo Park · 2021
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Helga Ingimundardottir and Thomas Philip Runarsson · 2018
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Achraf Oussidi and Azeddine Elhassouny · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Learning to Perform Local Rewriting for Combinatorial Optimization
Xinyun Chen and Yuandong Tian · 2019
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Dynamic job-shop scheduling in smart manufacturing using deep reinforcement learning
Libing Wang, Xin Hu, Yin Wang, Sujie Xu, Shijun Ma, Kexin Yang, Zhijun Liu, and Weidong Wang · 2021
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How Attentive are Graph Attention Networks?
Shaked Brody, Uri Alon, and Eran Yahav · 2022
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Learning the Quality of Machine Permutations in Job Shop Scheduling
Andrea Corsini, Simone Calderara, and Mauro Dell’Amico · 2022
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Learning to Control Local Search for Combinatorial Optimization
Jonas K Falkner, Daniela Thyssens, Ahmad Bdeir, and Lars Schmidt-Thieme · 2022
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Fast approximations for job shop scheduling: A lagrangian dual deep learning method
James Kotary, Ferdinando Fioretto, and Pascal Van Hentenryck · 2022
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A Simple Decentralized Cross-Entropy Method
Zichen Zhang, Jun Jin, Martin Jagersand, Jun Luo, and Dale Schuurmans · 2022
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Understanding Curriculum Learning in Policy Optimization for Online Combinatorial Optimization
Runlong Zhou, Yuandong Tian, Yi Wu, and Simon Shaolei Du · 2022
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A Deep Reinforcement Learning Framework Based on an Attention Mechanism and Disjunctive Graph Embedding for the Job-Shop Scheduling Problem
Ruiqi Chen, Wenxin Li, and Hongbing Yang · 2023
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Learning to control local search for combinatorial optimization
Jonas K. Falkner, Daniela Thyssens, Ahmad Bdeir, and Lars Schmidt-Thieme · 2023
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On the Study of Curriculum Learning for Inferring Dispatching Policies on the Job Shop Scheduling
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Self-Supervised Learning: Generative or Contrastive
Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang · 2023
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An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint Programming
Pierre Tassel, Martin Gebser, and Konstantin Schekotihin · 2023
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Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling
Cong Zhang, Zhiguang Cao, Wen Song, Yaoxin Wu, and Jie Zhang · 2024
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