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Solutions to the Traveling Salesperson Problem (TSP) have practical applications to processes in transportation, logistics, and automation, yet must be computed with minimal delay to satisfy the real-time nature of the underlying tasks.
Solution of a large-scale traveling-salesman problem
G. Dantzig, R. Fulkerson, and S. Johnson · 1954
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Shortest connection networks and some generalizations
Robert C. Prim · 1957
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A method for solving traveling-salesman problems
G. A. Croes · 1958
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An effective heuristic algorithm for the traveling-salesman problem
S. Lin and B. W. Kernighan · 1973
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Speech understanding systems: Summary of results of the five-year research effort at Carnegie-Mellon University
Dabbala Raj Reddy · 1977
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A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
M. D. McKay, R. J. Beckman, and W. J. Conover · 1979
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A branch-and-cut algorithm for the resolution of large-scale symmetric traveling salesman problems
Manfred Padberg and Giovanni Rinaldi · 1990
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A parallel route building algorithm for the vehicle routing and scheduling problem with time windows
Jean-Yves Potvin and Jean-Marc Rousseau · 1993
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Partial constraint satisfaction problems and guided local search
Chris Voudouris and Edward Tsang · 1996
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Neural networks for combinatorial optimization: A review of more than a decade of research
Kate Smith · 1999
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Guided local search and its application to the traveling salesman problem
Christos Voudouris and Edward Tsang · 1999
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Making best use of model evaluations to compute sensitivity indices
Andrea Saltelli · 2002
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Implementing the Dantzig-Fulkerson-Johnson algorithm for large traveling salesman problems
David Applegate, Robert Bixby, Vašek Chvátal, and William Cook · 2003
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The Traveling Salesman Problem: A Computational Study
David Applegate, Robert Bixby, Vašek Chvatál, and William Cook · 2006
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Greedy heuristics with regret, with application to the cheapest insertion algorithm for the TSP
Refael Hassin and Ariel Keinan · 2008
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General k-opt submoves for the Lin–Kernighan TSP heuristic
Keld Helsgaun · 2009
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Attention, learn to solve routing problems!
Wouter Kool, Herke Van Hoof, and Max Welling · 2018
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An efficient graph convolutional network technique for the travelling salesman problem
Chaitanya K Joshi, Thomas Laurent, and Xavier Bresson · 2019
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Emulation of physical processes with emukit
Andrei Paleyes, Mark Pullin, Maren Mahsereci, Neil Lawrence, and Javier Gonzalez · 2019
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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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Deep graph library: A graph-centric, highly-performant package for graph neural networks
Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang · 2019
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An extension of the Lin-Kernighan-Helsgaun TSP solver for constrained traveling salesman and vehicle routing problems
Keld Helsgaun · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Learning 2-opt heuristics for the traveling salesman problem via deep reinforcement learning
Paulo R de O da Costa, Jason Rhuggenaath, Yingqian Zhang, and Alp Akcay · 2020
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Learning improvement heuristics for solving routing problems
Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, and Andrew Lim · 2020
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Guidelines for the computational testing of machine learning approaches to vehicle routing problems, 2021
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Exponential-size neighborhoods for the pickup-and-delivery traveling salesman problem
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