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Towards resilience: Primal large-scale re-optimization
Transportation Research Part E: Logistics and Transportation Review ( IF 8.3 ) Pub Date : 2024-10-23 , DOI: 10.1016/j.tre.2024.103819 El Mehdi Er Raqabi, Yong Wu, Issmaïl El Hallaoui, François Soumis
Transportation Research Part E: Logistics and Transportation Review ( IF 8.3 ) Pub Date : 2024-10-23 , DOI: 10.1016/j.tre.2024.103819 El Mehdi Er Raqabi, Yong Wu, Issmaïl El Hallaoui, François Soumis
Perturbations are universal in supply chains, and their appearance has become more frequent in the past few years due to global events. These perturbations affect industries and could significantly impact production, quality, cost/profitability, and consumer satisfaction. In large-scale contexts, companies rely on operations research techniques. In such a case, re-optimization can support companies in achieving resilience by enabling them to simulate several what-if scenarios and adapt to changing circumstances and challenges in real-time. In this paper, we design a generic and scalable resilience re-optimization framework. We model perturbations, recovery decisions, and the resulting re-optimization problem, which maximizes resilience. We leverage the primal information through fixing, warm-start, valid inequalities, and machine learning. We conduct extensive computational experiments on a real-world, large-scale problem. The findings highlight that local optimization is enough to recover after perturbations and demonstrate the power of our proposed framework and solution methodology.
中文翻译:
迈向弹性:原始大规模重新优化
扰动在供应链中普遍存在,由于全球事件,它们的出现在过去几年变得更加频繁。这些扰动会影响各行各业,并可能显著影响生产、质量、成本/盈利能力和消费者满意度。在大规模环境中,公司依赖于运筹学技术。在这种情况下,重新优化可以通过使公司能够模拟多个假设场景并实时适应不断变化的环境和挑战来支持公司实现弹性。在本文中,我们设计了一个通用且可扩展的弹性再优化框架。我们对扰动、恢复决策和由此产生的重新优化问题进行建模,从而最大限度地提高弹性。我们通过修复、热启动、有效不等式和机器学习来利用原始信息。我们对现实世界的大规模问题进行了广泛的计算实验。研究结果强调,局部优化足以在扰动后恢复,并展示了我们提出的框架和解决方案方法的强大功能。
更新日期:2024-10-23
中文翻译:
迈向弹性:原始大规模重新优化
扰动在供应链中普遍存在,由于全球事件,它们的出现在过去几年变得更加频繁。这些扰动会影响各行各业,并可能显著影响生产、质量、成本/盈利能力和消费者满意度。在大规模环境中,公司依赖于运筹学技术。在这种情况下,重新优化可以通过使公司能够模拟多个假设场景并实时适应不断变化的环境和挑战来支持公司实现弹性。在本文中,我们设计了一个通用且可扩展的弹性再优化框架。我们对扰动、恢复决策和由此产生的重新优化问题进行建模,从而最大限度地提高弹性。我们通过修复、热启动、有效不等式和机器学习来利用原始信息。我们对现实世界的大规模问题进行了广泛的计算实验。研究结果强调,局部优化足以在扰动后恢复,并展示了我们提出的框架和解决方案方法的强大功能。