DAN: Decentralized Attention-based Neural Network for the MinMax Multiple Traveling Salesman Problem

INFORMS Journal on Computing 3

Reinelt, G.: TSPLIB—A Traveling Salesman Problem Library · 1991

Earlier work this paper cites.

Omega 34

Bektas, T.: The multiple traveling salesman problem: an overview of formulations and solution procedures · 2004

Earlier work this paper cites.

In: M. Gendreau, J.Y. Potvin (eds.) Handbook of Metaheuristics, vol. 146, pp. 321–361. Springer US, Boston, MA (2010)

Voudouris, C., Tsang, E.P., Alsheddy, A.: Guided Local Search · 2010

Earlier work this paper cites.

In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3741–3746. IEEE (2012)

Faigl, J., Kulich, M., Přeučil, L.: Goal assignment using distance cost in multi-robot exploration · 2012

Earlier work this paper cites.

URL https://developers.google.com/optimization/routing/vrp

Google: OR Tools (2012) · 2012

Earlier work this paper cites.

arXiv preprint arXiv:1506.03134 (2015)

Original

Vinyals, O., Fortunato, M., Jaitly, N.: Pointer networks · 2015

Earlier work this paper cites.

IEEE Robotics and Automation Letters 1

Oßwald, S., Bennewitz, M., Burgard, W., Stachniss, C.: Speeding-up robot exploration by exploiting background information · 2016

Earlier work this paper cites.

arXiv preprint arXiv:1611.09940 (2016)

Original

Bello, I., Pham, H., Le, Q.V., Norouzi, M., Bengio, S.: Neural combinatorial optimization with reinforcement learning · 2016

Earlier work this paper cites.

Roskilde: Roskilde University (2017)

Helsgaun, K.: An extension of the lin-kernighan-helsgaun tsp solver for constrained traveling salesman and vehicle routing problems · 2017

Cited alongside, same era.

In: Proceedings of NeurIPS, pp. 5998–6008 (2017)

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need · 2017

Cited alongside, same era.

In: Proceedings of AAMAS, pp. 66–83 (2017)

Gupta, J.K., Egorov, M., Kochenderfer, M.: Cooperative multi-agent control using deep reinforcement learning · 2017

Cited alongside, same era.

URL https://openai.com/blog/baselines-acktr-a2c/

OpenAI: OpenAI Baselines: ACKTR & A2C (2017) · 2017

Cited alongside, same era.

arXiv:1412.6980 (2017)

Original

Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization · 2017

Cited alongside, same era.

arXiv preprint arXiv:1803.09621 (2018)

In: Proceedings of OSDI, pp. 561–577 (2018)

Moritz, P., Nishihara, R., Wang, S., Tumanov, A., Liaw, R., Liang, E., Elibol, M., Yang, Z., Paul, W., Jordan, M.I., et al.: Ray: A distributed framework for emerging ai applications · 2018

Later among the works it cites.

arXiv:1907.11910 (2019)

Original

Lupoaie, V.I., Chili, I.A., Breaban, M.E., Raschip, M.: SOM-Guided Evolutionary Search for Solving MinMax Multiple-TSP · 2019

Later among the works it cites.

Knowledge-Based Systems 204

Hu, Y., Yao, Y., Lee, W.S.: A reinforcement learning approach for optimizing multiple traveling salesman problems over graphs · 2020

Later among the works it cites.

URL https://www.gurobi.com

Gurobi Optimizer (2020) · 2020

Later among the works it cites.

arXiv preprint arXiv:2106.03051 (2021)

Original

Park, J., Bakhtiyar, S., Park, J.: Schedulenet: Learn to solve multi-agent scheduling problems with reinforcement learning · 2021

Closest in time.