most citedMulti-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions

1 citations · 1 across the 2 of their papers we have counts for

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2 papers

cs.LG2025★ 1 cited

Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions

Thomas Hazenberg, Yao Ma, Seyed Sahand Mohammadi Ziabari +1

This study investigates how Multi-Agent Reinforcement Learning (MARL) can improve dynamic pricing strategies in supply chains, particularly in contexts where traditional ERP system…

physics.flu-dyn2023

Numerical determination of iron dust laminar flame speeds with the counterflow twin-flame technique

C. E. A. G. van Gool, T. Hazenberg, J. A. van Oijen +1

Iron dust counter-flow flames have been studied with the low-Mach-number combustion approximation. The model considers full coupling between the two phases, including particle/drop…