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Min-max routing problems aim to minimize the maximum tour length among multiple agents by having agents conduct tasks in a cooperative manner.
Sym-NCO: Leveraging symmetricity for neural combinatorial optimization
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The Euclidean travelling salesman problem is NP-complete
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Min–max vs. min–sum vehicle routing: A worst-case analysis
Bertazzi, L.; Golden, B.; and Wang, X. 2015 · 2015
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Tackling the bi-criteria facet of multiple traveling salesman problem with ant colony systems
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Pointer Networks
Vinyals, O.; Fortunato, M.; and Jaitly, N. 2015 · 2015
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Neural Combinatorial Optimization with Reinforcement Learning
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An Extension of the Lin-Kernighan-Helsgaun TSP Solver for Constrained Traveling Salesman and Vehicle Routing Problems
Helsgaun, K. 2017 · 2017
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Learning Combinatorial Optimization Algorithms over Graphs
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Attention is All you Need
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Learning to Perform Local Rewriting for Combinatorial Optimization
Chen, X.; and Tian, Y. 2019 · 2019
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Attention, Learn to Solve Routing Problems!
Kool, W.; van Hoof, H.; and Welling, M. 2019 · 2019
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OR-Tools
Perron, L.; and Furnon, V. 2019 · 2019
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Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning
da Costa, P. R. d. O.; Rhuggenaath, J.; Zhang, Y.; and Akcay, A. 2020 · 2020
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Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem
Hottung, A.; and Tierney, K. 2020 · 2020
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A reinforcement learning approach for optimizing multiple traveling salesman problems over graphs
Hu, Y.; Yao, Y.; and Lee, W. S. 2020 · 2020
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POMO: Policy optimization with multiple optima for reinforcement learning
Kwon, Y.-D.; Choo, J.; Kim, B.; Yoon, I.; Gwon, Y.; and Min, S. 2020 · 2020
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DAN: Decentralized Attention-based Neural Network for the MinMax Multiple Traveling Salesman Problem
Cao, Y.; Sun, Z.; and Sartoretti, G. 2021 · 2021
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A comprehensive survey on the Multiple Traveling Salesman Problem: Applications, approaches and taxonomy
Cheikhrouhou, O.; and Khoufi, I. 2021 · 2021
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Learning to Solve Routing Problems via Distributionally Robust Optimization
Jiang, Y.; Wu, Y.; Cao, Z.; and Zhang, J. 2022 · 2022
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Efficient Neural Neighborhood Search for Pickup and Delivery Problems
Ma, Y.; Li, J.; Cao, Z.; Song, W.; Guo, H.; Gong, Y.; and Chee, Y. M. 2022 · 2022
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Dimes: A differentiable meta solver for combinatorial optimization problems
Qiu, R.; Sun, Z.; and Yang, Y. 2022 · 2022
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Learning to solve multiple-TSP with time window and rejections via deep reinforcement learning
Zhang, R.; Zhang, C.; Cao, Z.; Song, W.; Tan, P. S.; Zhang, J.; Wen, B.; and Dauwels, J. 2022 · 2022
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Mapdp: Cooperative multi-agent reinforcement learning to solve pickup and delivery problems
Zong, Z.; Zheng, M.; Li, Y.; and Jin, D. 2022 · 2022
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Generalize a small pre-trained model to arbitrarily large TSP instances
Fu, Z.-H.; Qiu, K.-B.; and Zha, H. 2021 · 2021
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Efficient Active Search for Combinatorial Optimization Problems
Hottung, A.; Kwon, Y.-D.; and Tierney, K. 2021 · 2021
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Learning Collaborative Policies to Solve NP-hard Routing Problems
Kim, M.; Park, J.; and Kim, J. 2021 · 2021
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Deep Policy Dynamic Programming for Vehicle Routing Problems
Kool, W.; van Hoof, H.; Gromicho, J. A. S.; and Welling, M. 2021 · 2021
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Heterogeneous attentions for solving pickup and delivery problem via deep reinforcement learning
Li, J.; Xin, L.; Cao, Z.; Lim, A.; Song, W.; and Zhang, J. 2021 · 2021
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Learning to delegate for large-scale vehicle routing
Li, S.; Yan, Z.; and Wu, C. 2021 · 2021
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A hierarchical reinforcement learning based optimization framework for large-scale dynamic pickup and delivery problems
Ma, Y.; Hao, X.; Hao, J.; Lu, J.; Liu, X.; Xialiang, T.; Yuan, M.; Li, Z.; Tang, J.; and Meng, Z. 2021 · 2021
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A deep reinforcement learning approach for solving the traveling salesman problem with drone
Bogyrbayeva, A.; Yoon, T.; Ko, H.; Lim, S.; Yun, H.; and Kwon, C. 2023 · 2023
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Concorde TSP Solver
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SplitNet: a reinforcement learning based sequence splitting method for the MinMax multiple travelling salesman problem
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Learn to Solve the Min-max Multiple Traveling Salesmen Problem with Reinforcement Learning
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Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization
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Revisiting Sampling for Combinatorial Optimization
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Neural TSP solver with progressive distillation
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Towards Omni-generalizable Neural Methods for Vehicle Routing Problems
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Gurobi Optimizer Reference Manual
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