Towards resilience: Primal large-scale re-optimization

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Perturbations are universal in supply chains, and their appearance is getting more frequent in the past few years. These perturbations affect industries and could significantly impact production, quality, cost/profitability, and consumer satisfaction. In large-scale contexts, companies rely on mathematical optimization. Still, these companies must remain resilient to perturbations. In such a case, re-optimization can support companies in achieving resilience by enabling them to 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 to maximize 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 highlighting that local optimization is enough to recover after perturbations and demonstrating the power of our proposed framework and solution methodology.

, 27 pages

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