ABSTRACT We propose a dynamic optimization framework for Medical Oxygen Supply Chains (MOSCs) operating under concurrent disasters. The framework aims to minimize both the total response and transportation time, as well as the total operational cost. These goals are subject to constraints involving evacuation, capacity, maintenance, and inventory. Two models are developed: A deterministic baseline (Model 1) and an uncertainty‐aware spherical‐fuzzy formulation (Model 2). Multi‐objective solutions are obtained using several preference‐articulation and scalarization techniques. These include Fuzzy Goal Programming, Global Criterion, Weighted Average, Weighted Tchebycheff, Interactive Fuzzy Satisfying Technique, Neutrosophic Fuzzy Linear Programming, and Spherical Fuzzy Linear Programming, all implemented in LINGO. A case study on the Kumaon region in Uttarakhand, India, demonstrates the practical value of our approach. This region is prone to floods, landslides, and seismic events. Compared to Model 1, Model 2 yields a 9.96% reduction in transportation time (3124.365 2813.300) and an 8.83% reduction in total cost (194,200 177,052.2). Sensitivity analyses further evaluate robustness under demand surges, travel‐time disruptions, and capacity constraints. These analyses enable adaptive, real‐time decision support. The findings provide actionable guidance for emergency managers on resource allocation, risk mitigation, and preparedness. Ultimately, our framework enhances healthcare resilience and ensures timely oxygen delivery to medical facilities during compound crises.
Biswas et al. (Wed,) studied this question.
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