发表机构
Indian Institute of Technology Goa; School of Mathematics and Computer Science（印度理工学院果阿分校; 数学与计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
介绍开源Python库SupplyNetPy，用于任意供应链网络建模与离散事件仿真，支持多种策略和情况，组件可扩展。用户以图描述供应链，库处理仿真并提供报告，经详细验证能实现复杂模型编程生成与仿真等多种功能。
AI 中文摘要
本文介绍了SupplyNetPy，一个用于具有任意多级结构的供应链网络建模和离散事件仿真的开源、文档完善的Python库。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。所有组件可通过继承扩展。用户将供应链描述为具有节点和链路属性的图，库负责处理仿真并提供日志及详细的节点和网络级性能报告。文中介绍了其动机、设计、关键特性和架构，以及详细的验证结果。开发SupplyNetPy的一个关键动机是对复杂模型进行编程生成和仿真，以实现设计空间探索、假设分析、训练数据生成和供应链数字孪生。
英文摘要
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.
Comments14 pages, 6 figures, Winter Simulation Conference 2026
