Designing an efficient and equitable humanitarian supply chain dynamically via reinforcement learning

Weijia Jin

Designing an efficient and equitable humanitarian supply chain dynamically via reinforcement learning

Machine Learning 2025-05-26 v1 Artificial Intelligence

Authors: Weijia Jin

Abstract

This study designs an efficient and equitable humanitarian supply chain dynamically by using reinforcement learning, PPO, and compared with heuristic algorithms. This study demonstrates the model of PPO always treats average satisfaction rate as the priority.

Keywords

hyperparameter optimization optimization algorithm reinforcement learning

Cite

@article{arxiv.2505.17439,
  title  = {Designing an efficient and equitable humanitarian supply chain dynamically via reinforcement learning},
  author = {Weijia Jin},
  journal= {arXiv preprint arXiv:2505.17439},
  year   = {2025}
}

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