内生不确定性下的网络灵活性设计
Network Flexibility Design Under Endogenous Uncertainty
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中文总结 AI 辅助
针对供需不确定性，提出内生不确定性下的网络灵活性设计两阶段随机规划及无分布公式，通过分解算法高效求解，并揭示内生效应权衡对最优设计的影响。
中文摘要 AI 辅助
制造企业在供应和需求两方面都面临不确定性。它们实施流程灵活性，使工厂能够生产多种产品，以对冲供需不匹配的风险。将更多产品分配给一家工厂会导致该工厂的效率损失，而有限的灵活性可能因专业化效应而提高效率。此外，灵活性设计决定了产品的采购地点，从而因原产地效应和关税风险而影响需求。我们通过将供应和需求不确定性建模为内生的，即作为第一阶段设计决策的函数，将这些机制纳入供应网络灵活性设计问题中。我们提出了一个两阶段随机规划，并通过依赖于特定分布的最优性割的分解方案来求解。为避免对所有分布进行穷举枚举，我们推导了适用于所有分布以及供应和需求分布子集的无分布公式。通过大量计算实验，我们证明了我们的求解方法和无分布公式相对于穷举分布枚举和最新不等式分别具有优越性能。此外，我们解释了问题结构如何影响所提出公式的有效性。从管理角度来看，我们展示了内生效应之间的权衡及其如何影响最优灵活性设计。
英文摘要
Manufacturing firms face uncertainty in both supply and demand. They implement process flexibility, enabling plants to produce multiple products, to hedge against supply--demand mismatches. Assigning more products to a plant results in efficiency losses at the plant, whereas limited flexibility may improve efficiency because of the specialization effect. Furthermore, the flexibility design determines the sourcing locations of products, thereby affecting demand due to the country-of-origin effect and exposure to tariffs. We incorporate these mechanisms into the supply network flexibility design problem by modeling supply and demand uncertainties as endogenous, that is, as functions of the first-stage design decisions. We propose a two-stage stochastic program and solve it via a decomposition scheme that relies on distribution-specific optimality cuts. To avoid exhaustive enumeration of all distributions, we derive distribution-free formulations that are valid for all distributions and for subsets of supply and demand distributions. Through extensive computational experiments, we demonstrate the superior performance of our solution approach and distribution-free formulations relative to exhaustive distribution enumeration and state-of-the-art inequalities, respectively. Moreover, we explain how the problem structure affects the effectiveness of the proposed formulations. From a managerial perspective, we show the trade-offs between endogenous effects and how they affect the optimal flexibility design.
发表机构
- TUM School of Management, Technical University of Munich（慕尼黑工业大学管理学院）
- Munich Data Science Institute, Technical University of Munich（慕尼黑工业大学慕尼黑数据科学研究所）
- Department of Mathematical Sciences, University of Copenhagen（哥本哈根大学数学科学系）
- Department of Mathematics, Technical University of Munich（慕尼黑工业大学数学系）
机构由 AI 辅助整理，请以论文原文为准。
