生成合成数据集SynDelay，助力物流延迟预测研究。
SynDelay: A Synthetic Dataset for Delivery Delay Prediction
- 用真实数据训练的生成模型合成物流数据，保留真实模式。
- 提供基准结果与评估指标，支持模型对比测试。
- 开源可共享，适合供应链AI研究者使用。
人工智能正在重塑供应链管理，但配送延迟预测等任务的发展仍受限于高质量、公开可用数据集的缺乏。现有数据集往往为专有、规模小或维护不一致，阻碍了可复现性与基准测试。本文提出SynDelay，一个用于配送延迟预测的合成数据集。该数据集采用先进生成模型，基于真实世界数据生成，既保留了真实的配送模式，又确保了隐私安全。尽管存在部分噪声和不一致性，但仍构成一个具有挑战性且实用的测试平台。为促进应用，我们提供了基线结果与评估指标作为参考基准，而非宣称达到最先进水平。SynDelay可通过供应链数据枢纽（Supply Chain Data Hub）公开获取，这是一个推动供应链人工智能数据共享与基准测试的开放倡议。我们鼓励社区贡献数据集、模型与评估方法，共同推进该领域研究。所有代码均在https://supplychaindatahub.org 公开可访问。
原文摘要 · Abstract (English)
Artificial intelligence (AI) is transforming supply chain management, yet progress in predictive tasks -- such as delivery delay prediction -- remains constrained by the scarcity of high-quality, openly available datasets. Existing datasets are often proprietary, small, or inconsistently maintained, hindering reproducibility and benchmarking. We present SynDelay, a synthetic dataset designed for delivery delay prediction. Generated using an advanced generative model trained on real-world data, SynDelay preserves realistic delivery patterns while ensuring privacy. Although not entirely free of noise or inconsistencies, it provides a challenging and practical testbed for advancing predictive modelling. To support adoption, we provide baseline results and evaluation metrics as initial benchmarks, serving as reference points rather than state-of-the-art claims. SynDelay is publicly available through the Supply Chain Data Hub, an open initiative promoting dataset sharing and benchmarking in supply chain AI. We encourage the community to contribute datasets, models, and evaluation practices to advance research in this area. All code is openly accessible at https://supplychaindatahub.org.
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