Viable Supply Chain Network Design: Machine Learning-Derived Chance-Constrained Programming

This dataset contains the computational instances used in the study on two-echelon supply chain network design under facility disruptions. The instances are designed to evaluate models that incorporate cross-echelon dependencies, facility unreliability, and integrated strategies for network viability, including resilience, agility, and environmental considerations. The dataset includes parameterized instances representing different network sizes and configurations, covering small-, medium-, and large-scale problems. Each instance specifies facility locations, customer zones, demand levels, disruption probabilities, capacity limits, fixed and transportation costs, emissions parameters, and service requirements. The data are structured to support the implicit mixed-integer programming formulations in all sizes and also the scenario-based for the small and medium size problems. Additionally, the dataset includes: Generated disruption scenarios for the scenario-based formulation. Training and testing data used for machine learning models that approximate chance constraints. The instances are intended to facilitate reproducibility of the computational experiments and to support further research on reliable and sustainable supply chain network design under uncertainty. Researchers can use this dataset to benchmark optimization models, test decomposition or heuristic approaches, and explore machine learning–enhanced optimization techniques in stochastic and disruption-prone environments.本数据集收录了设施中断场景下两级供应链网络设计研究中所用的计算实例。此类实例旨在评估纳入跨层级依赖关系、设施不可靠性，以及涵盖韧性、敏捷性与环境考量在内的网络生存性整合策略的各类模型。 本数据集包含表征不同网络规模与配置的参数化实例，涵盖小规模、中规模及大规模优化问题。各实例均明确规定了设施选址、客户分区、需求水平、中断概率、容量限制、固定成本与运输成本、排放参数及服务要求。该数据集的结构可适配全规模下的隐式混合整数规划建模需求，同时也可为中小型规模问题提供基于场景的建模支持。 此外，本数据集还涵盖：用于基于场景建模的生成式中断场景，以及用于近似机会约束的机器学习模型训练与测试数据。 本数据集旨在助力计算实验的可复现性，并为不确定性环境下可靠且可持续的供应链网络设计领域的后续研究提供支撑。研究人员可借助该数据集对优化模型开展基准测试、验证分解法或启发式求解方法，并探索随机且易发生中断场景下融合机器学习的优化技术。