Abstract
Resilience is becoming a top priority across industrial sectors, with increasing pressures to assess it systematically. In this work, we present an optimization-based framework for proactive design and planning under uncertainty of multi-product manufacturing networks, and testing of the reactive strategies available to withstand unforeseen disruptions. Specifically, the design problem is formulated as a two-stage stochastic optimization, integrating multi-period planning and scheduling, aimed towards mitigation against uncertainty. Designs are then fixed and tested through simulated outcomes from out-of-sample uncertainty distributions, with feasibility of operation monitored through the time-to-recover post disruption. Infeasibility triggers a scenario-update procedure via ??-means clustering, whereby critical uncertainty information based on simulated outcomes is integrated in the proactive planning step, including low-probability high-impact scenarios. Modular and non-modular designs are compared to quantify the value of recourse actions with respect to responsiveness to disruptions. Results highlight that adaptable modular designs enable shorter recovery times combined with lower cost commitments. Overall, the framework underpins the development of tools for the anticipation and mitigation of disruptive events and systematic re-design based on continuously updated uncertainty information.