We address the dynamic design of supply chain networks in which the moments of demand distribution function are uncertain and facilities’ availability is stochastic because of possible disruptions. To incorporate the existing stochasticity in our dynamic problem, we develop a multi-stage stochastic program to specify the optimal location, capacity, inventory, and allocation decisions. Further, a data-driven rolling horizon approach is developed to use observations of the random parameters in the stochastic optimization problem. In contrast to traditional stochastic programming approaches that are valid only for a limited number of scenarios, the rolling horizon approach makes the determined decisions by the stochastic program implementable in practice and evaluates them. The stochastic program is presented as a quadratic conic optimization, and to generate an efficient scenario tree, a forward scenario tree construction technique is employed. An extensive numerical study is carried out to investigate the applicability of the presented model and rolling horizon procedure, the efficiency of risk-measurement policies, and the performance of the scenario tree construction technique. Several key practical and managerial insights related to the dynamic supply chain network design under uncertainty are gained based on the computational results.

中文翻译：

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数据驱动的滚动地平线方法在中断和需求不确定下动态设计供应链分销网络

我们解决了供应链网络的动态设计，其中需求分布函数的时刻是不确定的，并且由于可能的中断，设施的可用性是随机的。为了将现有的随机性纳入我们的动态问题，我们开发了一个多阶段随机程序来指定最佳位置、容量、库存和分配决策。此外，开发了一种数据驱动的滚动水平方法来使用随机优化问题中随机参数的观察。与仅对有限数量的场景有效的传统随机规划方法相比，滚动水平方法使随机程序确定的决策在实践中可实施并对其进行评估。随机程序表示为二次圆锥优化，为了生成高效的场景树，采用了前向场景树构建技术。进行了广泛的数值研究，以研究所提出的模型和滚动水平程序的适用性、风险测量策略的效率以及情景树构建技术的性能。基于计算结果，获得了与不确定性下的动态供应链网络设计相关的几个关键实践和管理见解。