Researchers introduce the Online Shared Supply Allocation (OSSA) problem, a theoretical framework for allocating limited resources across multiple locations before demand is known, common in humanitarian logistics and vaccine distribution. The proposed GPA algorithm achieves a 4/3-approximation ratio to optimal offline solutions, with proven tight bounds and a learning-augmented variant that incorporates forecasts.
This research addresses a fundamental challenge in resource allocation systems where prepositioned supply must be distributed across geographically dispersed locations without knowledge of actual demand patterns. The OSSA framework extends classical inventory theory by eliminating backlogging—a critical constraint in humanitarian contexts where unmet demand represents permanent service losses rather than delayed fulfillment. The theoretical contribution centers on the GPA algorithm's proven 4/3-approximation guarantee, establishing both upper and lower bounds that demonstrate the inherent difficulty of the problem without perfect information. The additive-error term independent of total supply suggests that system performance degrades gracefully even under severe scarcity conditions, which has practical implications for crisis response scenarios with genuinely constrained resources.
The learning-augmented extension represents a bridge between pure algorithmic approaches and real-world deployment realities, where domain experts and machine learning models typically provide imperfect demand forecasts. By formally incorporating prediction quality into the algorithmic framework, the work acknowledges that practitioners combine multiple information sources rather than relying solely on worst-case algorithm design. This principled approach to leveraging forecasts while maintaining robustness against poor predictions addresses a gap between academic algorithm design and operational decision-making in supply chain management.
For practitioners in logistics, public health, and disaster response, this research provides theoretical validation that threshold-based allocation policies can achieve near-optimal performance despite fundamental information asymmetries. The experimental validation on both synthetic and real-world datasets demonstrates that simple, interpretable policies outperform natural baselines when supply scarcity becomes acute. Organizations implementing emergency resource distribution systems could benefit from the algorithm's transparency and theoretical guarantees, though deployment would require customization for specific operational constraints and cost structures unique to each domain.
- →GPA algorithm achieves proven 4/3-approximation to offline optimal solutions with unavoidable additive error terms in resource allocation under uncertainty.
- →Learning-augmented variant enables practical integration of imperfect forecasts while maintaining robustness against poor predictions.
- →Theoretical framework formalizes resource allocation challenges in humanitarian logistics, vaccine distribution, and disaster response without backlogging capability.
- →Matching lower bounds confirm the 4/3 ratio is tight, establishing fundamental limits on algorithm performance for this problem class.
- →Experimental results show GPA outperforms natural baselines particularly when global supply becomes scarce across multiple allocation sites.