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Supply chain management (SCM) involves coordinating the flow of goods, information, and finances across various entities to deliver products efficiently.
The beer distribution game: using simulation to teach systems thinking
Goodwin, J. S.; and Franklin, S. G. 1994 · 1994
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Information distortion in a supply chain: The bullwhip effect
Lee, H. L.; Padmanabhan, V.; and Whang, S. 1997 · 1997
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Agent-based supply chain integration
Nissen, M. E. 2001 · 2001
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Multi-agent based supply chain modelling with dynamic environment
Kaihara, T. 2003 · 2003
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Multi-agent coordination based on tokens: Reduction of the bullwhip effect in a forest supply chain
Moyaux, T.; Chaib-Draa, B.; and D’Amours, S. 2003 · 2003
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Or-gym: A reinforcement learning library for operations research problems
Hubbs, C. D.; Perez, H. D.; Sarwar, O.; Sahinidis, N. V.; Grossmann, I. E.; and Wassick, J. M. 2020 · 2008
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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.; Sun, M.; and Whiteson, S. 2020 · 2011
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A mathematical model of the beer game
Edali, M.; and Yasarcan, H. 2014 · 2014
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Proximal policy optimization algorithms
Schulman, J.; Wolski, F.; Dhariwal, P.; Radford, A.; and Klimov, O. 2017 · 2017
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RLlib: Abstractions for Distributed Reinforcement Learning
Liang, E.; Liaw, R.; Nishihara, R.; Moritz, P.; Fox, R.; Goldberg, K.; Gonzalez, J. E.; Jordan, M. I.; and Stoica, I. 2018 · 2018
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Adaptive supply chain: Demand–supply synchronization using deep reinforcement learning
Kegenbekov, Z.; and Jackson, I. 2021 · 2021
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A deep q-network for the beer game: Deep reinforcement learning for inventory optimization
Oroojlooyjadid, A.; Nazari, M.; Snyder, L. V.; and Takáč, M. 2022 · 2022
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The surprising effectiveness of ppo in cooperative multi-agent games
Yu, C.; Velu, A.; Vinitsky, E.; Gao, J.; Wang, Y.; Bayen, A.; and Wu, Y. 2022 · 2022
Cited alongside, same era.
Achiam, J.; Adler, S.; Agarwal, S.; Ahmad, L.; Akkaya, I.; Aleman, F. L.; Almeida, D.; Altenschmidt, J.; Altman, S.; Anadkat, S.; et al. 2023 · 2023
Cited alongside, same era.
Improving Multi-Agent Reinforcement Learning for Beer Game by Reward Design Based on Payment Mechanism
Hori, M.; and Matsui, T. 2023 · 2023
Cited alongside, same era.
Rethinking the Buyer’s Inspection Paradox in Information Markets with Language Agents
Weiss, M.; Rahaman, N.; Wuthrich, M.; Bengio, Y.; Li, L. E.; Schölkopf, B.; and Pal, C. 2023 · 2023
Later among the works it cites.
Autogen: Enabling next-gen llm applications via multi-agent conversation framework
Wu, Q.; Bansal, G.; Zhang, J.; Wu, Y.; Zhang, S.; Zhu, E.; Li, B.; Jiang, L.; Zhang, X.; and Wang, C. 2023 · 2023
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Competeai: Understanding the competition behaviors in large language model-based agents
Zhao, Q.; Wang, J.; Zhang, Y.; Jin, Y.; Zhu, K.; Chen, H.; and Xie, X. 2023 · 2023
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Theoretical approaches to AI in supply chain optimization: Pathways to efficiency and resilience
Abaku, E. A.; Edunjobi, T. E.; and Odimarha, A. C. 2024 · 2024
Closest in time.
Large language model based multi-agents: A survey of progress and challenges
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Alympics: Language agents meet game theory
Mao, S.; Cai, Y.; Xia, Y.; Wu, W.; Wang, X.; Wang, F.; Ge, T.; and Wei, F. 2023 · 2023
Cited alongside, same era.
Predictive Demand Disruption Signals for Supply Chain Networks
Quan, Y.; Pothen, A.; and Montreuil, B. 2023 · 2023
Cited alongside, same era.
An empirical study on using large language models to analyze software supply chain security failures
Singla, T.; Anandayuvaraj, D.; Kalu, K. G.; Schorlemmer, T. R.; and Davis, J. C. 2023 · 2023
Cited alongside, same era.
Gymnasium
Towers, M.; Terry, J. K.; Kwiatkowski, A.; Balis, J. U.; Cola, G. d.; Deleu, T.; Goulão, M.; Kallinteris, A.; KG, A.; Krimmel, M.; Perez-Vicente, R.; Pierré, A.; Schulhoff, S.; Tai, J. J.; Shen, A. T. J.; and Younis, O. G. 2023 · 2023
Cited alongside, same era.
Large language models for supply chain optimization
Li, B.; Mellou, K.; Zhang, B.; Pathuri, J.; and Menache, I. 2023a
Cited in the paper.
Large language model-empowered agents for simulating macroeconomic activities
Li, N.; Gao, C.; Li, Y.; and Liao, Q. 2023b
Cited in the paper.
Li, Y.; Yu, Y.; Li, H.; Chen, Z.; and Khashanah, K. 2023c
Cited in the paper.
Guo, T.; Chen, X.; Wang, Y.; Chang, R.; Pei, S.; Chawla, N. V.; Wiest, O.; and Zhang, X. 2024 · 2024
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An analysis of multi-agent reinforcement learning for decentralized inventory control systems
Mousa, M.; van de Berg, D.; Kotecha, N.; del Rio-Chanona, E. A.; and Mowbray, M. 2024 · 2024
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Quan, Y.; and Liu, Z. 2024 · 2024
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Supply Chain Management: Ensuring Seamless Operations
Yasmin, G. 2024 · 2024
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FinMem: A performance-enhanced LLM trading agent with layered memory and character design
Yu, Y.; Li, H.; Chen, Z.; Jiang, Y.; Li, Y.; Zhang, D.; Liu, R.; Suchow, J. W.; and Khashanah, K. 2024 · 2024
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