The ongoing advancement of 3D printing technology provides an innovative approach to addressing challenges in disaster relief operations. By utilizing a variety of printing materials, 3D printers can produce essential disaster relief resources needed for disaster relief, effectively satisfying the varied demands that arise after disasters. This paper examines the joint optimization of pre-disaster and post-disaster humanitarian operations. Given the significant unpredictability of natural disasters, we introduce a two-stage distributionally robust optimization model to tackle the uncertainty in the demand for various relief resources. The first stage of the model involves decisions related to pre-disaster facility location, 3D printer deployment, and resource allocation. The second stage model addresses the post-disaster rescue activities, including decisions on the production and transportation decisions of relief resources. To address demand uncertainty, we propose an ambiguity set using the Wasserstein metric and reformulate the two-stage distributionally robust optimization model into a tractable formulation. To solve this problem, we employ a Benders decomposition algorithm with an acceleration strategy. The performance of our proposed model and algorithm is evaluated via a real-world case. Numerical experiments reveal that our distributionally robust optimization model outperforms the benchmark model across various metrics. Additionally, we conduct a series of effect analyses and provide managerial insights for decision-makers involved in disaster relief operations.

中文翻译：

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使用 3D 打印解决灾前设施位置问题的分布鲁棒优化


3D 打印技术的不断进步为应对救灾行动中的挑战提供了一种创新方法。通过利用各种打印材料，3D 打印机可以生产出救灾所需的基本救灾资源，有效满足灾后出现的各种需求。本文研究了灾前和灾后人道行动的联合优化。鉴于自然灾害的重大不可预测性，我们引入了一个两阶段分布稳健优化模型，以解决对各种救灾资源需求的不确定性。模型的第一阶段涉及与灾前设施位置、3D 打印机部署和资源分配相关的决策。第二阶段模型涉及灾后救援活动，包括对救援资源的生产和运输决策。为了解决需求不确定性，我们提出了一个使用 Wasserstein 度量的模糊集，并将两阶段分布稳健优化模型重新表述为易于处理的公式。为了解决这个问题，我们采用了带有加速策略的 Benders 分解算法。我们提出的模型和算法的性能是通过真实案例进行评估的。数值实验表明，我们的分布稳健优化模型在各种指标上都优于基准模型。此外，我们还进行一系列效果分析，并为参与救灾行动的决策者提供管理见解。