MULTI-OBJECTIVE RESOURCE ALLOCATION FOR RESILIENT FINANCIAL, HEALTHCARE, RETAIL, AND LOGISTICS SERVICE NETWORKS

This study proposes an Explainable Multi-Objective Decision Intelligence (MODI) architecture for resilient resource allocation across financial, healthcare, retail, and logistics service networks. The framework addresses resource allocation challenges associated with demand uncertainty, fraud exposure, capacity limitations, supplier disruption, operational failures, and restricted access to sensitive organizational data. A quantitative, model based methodology is developed using synthetic service-network datasets. The proposed framework integrates permissioned data sharing, Evidential Reasoning, bi-objective stochastic programming, fuzzy regulatory constraints, Weighted K-means scenario reduction, adaptive surrogate modeling, and α-Pareto analysis. Financial transactions, healthcare claims, retail demand and inventory, supplier conditions, workforce requirements, and logistics capacity are represented within a common decision environment. Scenario analysis evaluates normal and adverse operating conditions, while Pareto optimization provides alternative resource risk solutions. The evaluation examines resource cost, service level, risk exposure, capacity utilization, computational performance, scenario-reduction error, and decision explainability. The illustrative results indicate that the proposed framework can represent resource risk trade-offs, support coordinated allocation under disruption, reduce scenario complexity, and connect optimization outcomes with interpretable risk evidence. The study provides a model based foundation for explainable resource governance across interconnected service networks.