A Soft Computing-Based Approach for Intelligent Resource Allocation Under Flexible Constraints in Disaster Relief

(1) Background: In various disaster-relief contexts, the demand for resources often exceeds the available supply. Fuzzy optimization approaches address these situations by offering solutions that balance trade-offs between the objective function and flexible constraint satisfaction. (2) Methods: In this article, we present a novel soft computing approach for such situations, comprising a model that extends the Fuzzy Variable Size and Cost Bin Packing Problem to account for the relevance of each resource. Additionally, we propose a flexible, metaheuristic-based solution strategy that provides decision-makers with multiple alternatives, thereby enabling more informed decision-making. (3) Results: Some experimental results are presented to illustrate the benefits of the proposed approach. It is demonstrated that Pareto sets containing diverse non-dominated solutions can be obtained within a few seconds, including alternatives where the packing cost is substantially reduced through various combinations of constraint relaxations. (4) Conclusions: The proposed method enables flexible determination of the optimal packing of items in each situation by considering cost, the items to be packed, priority fulfillment, and a slight bin overload.