Electricity shortages triggered by extreme events have profoundly disrupted supply chains (SCs) in recent years. As SC operations heavily depend on electricity, simultaneous uncertainties may occur in supply and production capacities, demand, and transportation capacity if electric trucks are used. To enhance SC resilience, it is imperative to combine new recovery strategies (i.e. alternative energy sources and alternative transportation vehicles (ATVs)) with traditional ones to restore SC performance. However, these strategies have not been investigated quantitatively. Besides, only partial probability distribution information of uncertainties may be available under electricity shortages. Thus, this work improves SC resilience from a worst-case perspective, where the moment information is known. For the problem, a bi-objective distributionally robust chance-constrained optimisation model is constructed to balance the total cost and the service level. Then, an effective ϵ-model-based constructive heuristic (ϵ-MBCH) is developed. Compared with the ϵ-constraint method, the efficiency of ϵ-MBCH is improved by 99.81%. Based on stress tests and sensitivity analyses on a real case, key managerial insights include: (i) the ATV strategy is indispensable under varying severities of electricity shortages; (ii) the inventory management strategy helps reduce reliance on power generators and diesel trucks; and (iii) mitigating demand variability is essential for reducing costs. [ABSTRACT FROM AUTHOR]

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