Hierarchical production control and distribution planning under retail uncertainty with reinforcement learning

International Journal of Production Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: Jan. 18, 2025

Effective coordination between production control and distribution planning is critical in supply chain management. However, existing research mainly focuses on responding to stochastic demand, while the impact of uncertain retail capabilities often overlooked. This study proposes a hierarchical framework that integrates coordinates explicitly addressing uncertainty capabilities. Specifically, we develop reinforcement learning (RL) algorithm learns under adaptive (upper level) (lower level). information then fed into framework, which enhances performance both layers maximise system profit considering opportunity costs holding costs. Moreover, incorporate novel function based exponential penalty term reward effectively enforce side constraint inventory capacity. approach enables RL derive feasible policies thereby enhance training process. We evaluate proposed controller through case utilising real-world transaction data from steel manufacturing industry. The results demonstrate accurate identification can facilitate management market conditions. Furthermore, improve overall profits by coordinating actions different strategies.

Language: Английский

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Collaborative production control and distributor selection via multi-agent reinforcement learning with differentiable communication

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127539 - 127539

Published: April 1, 2025

Language: Английский

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A Markov decision process framework for order dispatching in on-demand delivery services

Multimodal Transportation, Journal Year: 2025, Volume and Issue: unknown, P. 100240 - 100240

Published: April 1, 2025

Language: Английский

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