开源工具包，可模拟任意供应链结构与库存网络。
SupplyNetPy: An Open-Source Python Library for High-Fidelity Modeling and Simulation of Arbitrary Supply Chain and Inventory Networks
- 基于图结构建模供应链，支持多级、动态补货与节点中断。
- 验证显示结果与理论值、商业软件及案例研究高度一致。
- 适合做供应链设计探索、数字孪生和训练数据生成。
本文介绍 SupplyNetPy，一个开源、文档齐全的 Python 库，用于对具有任意多级结构的供应链与库存网络进行建模与离散事件仿真。它支持多种补货策略、易腐库存、节点中断以及随机需求和提前期。所有组件均可通过继承扩展。用户以带属性的图形式描述供应链，库则负责仿真，并提供节点与网络级别的详细日志与绩效报告。本文阐述了 SupplyNetPy 的动机、设计、核心特性与架构，并展示了详细的验证结果（对比解析基准、商业工具和已发表案例研究）。开发 SupplyNetPy 的关键动因是实现复杂模型的程序化生成与仿真，支持设计空间探索、情景分析、训练数据生成及供应链数字孪生。
原文摘要 · Abstract (English)
This paper introduces SupplyNetPy, an open-source, well-documented Python library for modeling and discrete-event simulation of supply chain networks with arbitrary multi-echelon structures. It supports multiple replenishment policies, perishable inventory, node disruptions, and stochastic demand and lead times. All components are extensible via inheritance. Users describe a supply chain as a graph with node and link attributes, while the library handles simulation, providing logs and extensive node and network level performance reports. This paper presents the motivation, design, key features, and architecture of SupplyNetPy, along with detailed validation results (against analytical benchmarks, a commercial tool, and a published case study). A key motivation behind SupplyNetPy's development is programmatic generation and simulation of complex models, enabling design-space exploration, what-if analysis, training data generation, and supply chain digital twins.
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