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Most solutions to the inventory management problem assume a centralization of information that is incompatible with organisational constraints in real supply chain networks.
The starcraft multi-agent challenge
Samvelyan, M., Rashid, T., de Witt, C.S., Farquhar, G., Nardelli, N., Rudner, T.G.J., Hung, C.M., Torr, P.H.S., Foerster, J., Whiteson, S., 2019 · 1902
Earlier work this paper cites.
Global optimality guarantees for policy gradient methods
Bhandari, J., Russo, D., 2019 · 1906
Earlier work this paper cites.
The dynamic lot-size model with stochastic lead times
Nevison, C., Burstein, M., 1984 · 1984
Earlier work this paper cites.
Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment
Sterman, J.D., 1989 · 1989
Earlier work this paper cites.
Backpropagation through time: what it does and how to do it
Werbos, P., 1990 · 1990
Earlier work this paper cites.
Optimal order policies in assembly systems with random demand and random supplier delivery
Gurnani, H., Akella, R., Lehoczky, J., 1996 · 1996
Earlier work this paper cites.
Long Short-Term Memory
Hochreiter, S., Schmidhuber, J., 1997 · 1997
Earlier work this paper cites.
The dynamics of reinforcement learning in cooperative multiagent systems, in: Proceedings of the Fifteenth National/Tenth Conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence, American Association for Artificial Intelligence, USA. p. 746–752
Claus, C., Boutilier, C., 1998 · 1998
Earlier work this paper cites.
Decentralized inventory control in a two-level distribution system
Andersson, J., Marklund, J., 2000 · 2000
Earlier work this paper cites.
Flow coordination and information sharing in supply chains: review, implications, and directions for future research
Sahin, F., Robinson, E.P., 2002 · 2002
Earlier work this paper cites.
Information distortion in a supply chain: The bullwhip effect
Lee, H.L., Padmanabhan, V., Whang, S., 2004 · 2004
Earlier work this paper cites.
Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Levine, S., Kumar, A., Tucker, G., Fu, J., 2020 · 2005
Earlier work this paper cites.
Approximate Dynamic Programming: Solving the curses of dimensionality. volume 703
Powell, W.B., 2007 · 2007
Earlier work this paper cites.
Solving deep memory pomdps with recurrent policy gradients, in: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (Eds.), Artificial Neural Networks – ICANN 2007, Springer Berlin Heidelberg, Berlin, Heidelberg. pp. 697–706
Wierstra, D., Foerster, A., Peters, J., Schmidhuber, J., 2007 · 2007
Earlier work this paper cites.
Or-gym: A reinforcement learning library for operations research problems
Hubbs, C.D., Perez, H.D., Sarwar, O., Sahinidis, N.V., Grossmann, I.E., Wassick, J.M., 2020 · 2008
Earlier work this paper cites.
Robust multi-echelon multi-period inventory control
Aharon, B.T., Boaz, G., Shimrit, S., 2009 · 2009
Earlier work this paper cites.
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
Boyd, S., Parikh, N., Chu, E., Eckstein, J., Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J., 2010 · 2010
Earlier work this paper cites.
Is independent learning all you need in the starcraft multi-agent challenge?
de Witt, C.S., Gupta, T., Makoviichuk, D., Makoviychuk, V., Torr, P.H.S., Sun, M., Whiteson, S., 2020 · 2011
Earlier work this paper cites.
The complexity of decentralized control of markov decision processes
Bernstein, D.S., Zilberstein, S., Immerman, N., 2013 · 2013
Earlier work this paper cites.
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., Zaremba, W., 2016 · 2016
Cited alongside, same era.
Recent advances in mathematical programming techniques for the optimization of process systems under uncertainty
Grossmann, I.E., Apap, R.M., Calfa, B.A., García-Herreros, P., Zhang, Q., 2016 · 2016
Cited alongside, same era.
Multi-agent reinforcement learning as a rehearsal for decentralized planning
Kraemer, L., Banerjee, B., 2016 · 2016
Cited alongside, same era.
Learning multiagent communication with backpropagation
Sukhbaatar, S., Szlam, A., Fergus, R., 2016 · 2016
Cited alongside, same era.
Distributed mpc for dynamic supply chain management
Dunbar, W.B., Desa, S., 2017 · 2017
Cited alongside, same era.
Graph convolutional reinforcement learning, in: International Conference on Learning Representations
Jiang, J., Dun, C., Huang, T., Lu, Z., 2020 · 2020
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Towards heterogeneous multi-agent reinforcement learning with graph neural networks
Meneghetti, D., Bianchi, R., 2020 · 2020
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Two-stage distributionally robust optimization for maritime inventory routing
Liu, B., Zhang, Q., Yuan, Z., 2021 · 2021
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Algorithmic approaches to inventory management optimization
Perez, H.D., Hubbs, C.D., Li, C., Grossmann, I.E., 2021 · 2021
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Federated reinforcement learning: Techniques, applications, and open challenges
Qi, J., Zhou, Q., Lei, L., Zheng, K., 2021 · 2021
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Relations between model predictive control and reinforcement learning
Görges, D., 2017 · 2017
Cited alongside, same era.
Population based training of neural networks
Jaderberg, M., Dalibard, V., Osindero, S., Czarnecki, W.M., Donahue, J., Razavi, A., Vinyals, O., Green, T., Dunning, I., Simonyan, K., Fernando, C., Kavukcuoglu, K., 2017 · 2017
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J., 2017 · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O., 2017 · 2017
Cited alongside, same era.
Optimal policies for a dual-sourcing inventory problem with endogenous stochastic lead times
Song, J.S., Xiao, L., Zhang, H., Zipkin, P., 2017 · 2017
Cited alongside, same era.
Spinning Up in Deep Reinforcement Learning
Achiam, J., 2018 · 2018
Cited alongside, same era.
Deep multi-agent reinforcement learning using dnn-weight evolution to optimize supply chain performance, in: 51st Hawaii International Conference on System Sciences
Fuji, T., Ito, K., Matsumoto, K., Yano, K., 2018 · 2018
Cited alongside, same era.
Yu, C., Velu, A., Vinitsky, E., Wang, Y., Bayen, A., Wu, Y., 2021 · 2021
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Supply planning and inventory control under lead time uncertainty: a literature review and future directions
Ben-Ammar, O., Dolgui, A., Hnaien, F., Ould-Louly, M.A., 2022 · 2022
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Deep reinforcement learning for inventory control: A roadmap
Boute, R.N., Gijsbrechts, J., Van Jaarsveld, W., Vanvuchelen, N., 2022 · 2022
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Coordination, cooperation, and collaboration in production-inventory systems: a systematic literature review
Ghasemi, E., Lehoux, N., Rönnqvist, M., 2022 · 2022
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Can deep reinforcement learning improve inventory management? performance on lost sales, dual-sourcing, and multi-echelon problems
Gijsbrechts, J., Boute, R.N., Van Mieghem, J.A., Zhang, D.J., 2022 · 2022
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Deep reinforcement learning with shallow controllers: An experimental application to pid tuning
Lawrence, N.P., Forbes, M.G., Loewen, P.D., McClement, D.G., Backström, J.U., Gopaluni, R.B., 2022 · 2022
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Feedback-based deterministic optimization is a robust approach for supply chain management under demand uncertainty
Lejarza, F., Kelley, M.T., Baldea, M., 2022 · 2022
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Multi-agent deep reinforcement learning for multi-echelon inventory management
Liu, X., Hu, M., Peng, Y., Yang, Y., 2022 · 2022
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Madeka, D., Torkkola, K., Eisenach, C., Luo, A., Foster, D.P., Kakade, S.M., 2022 · 2022
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Industrial data science–a review of machine learning applications for chemical and process industries
Mowbray, M., Vallerio, M., Perez-Galvan, C., Zhang, D., Chanona, A.D.R., Navarro-Brull, F.J., 2022 · 2022
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Robust optimization approaches for purchase planning with supplier selection under lead time uncertainty
Thevenin, S., Ben-Ammar, O., Brahimi, N., 2022 · 2022
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Integrated supplier selection, scheduling, and routing problem for perishable product supply chain: A distributionally robust approach
Hashemi-Amiri, O., Ghorbani, F., Ji, R., 2023 · 2023
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Distributional reinforcement learning for inventory management in multi-echelon supply chains
Wu, G., de Carvalho Servia, M.Á., Mowbray, M., 2023 · 2023
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