Spatial decision support system for national logistics planning: a Tri-Level optimization framework integrating geospatial analysis, uncertainty modelling, and sustainability metrics

Efficient spatial planning of logistics infrastructure under deep uncertainty remains a critical challenge in geo-information science. This study advances spatial decision modelling by proposing an Iterative Tri-Level Optimization Framework that integrates Geographic Information Systems (GIS), the Best-Worst Method (BWM), and Z-number Data Envelopment Analysis (Z-number DEA) within a closed-loop, meta-optimized system. The framework operates through three interconnected levels: (1) base evaluation of geographic suitability (Gi), operational efficiency (Ei), and Spatial Risk Index (Ri) for 59 candidate sites across Iran; (2) portfolio synthesis via a Composite Decision Index CDIi and selection of Top−15 networks using a Network Performance Score NPS that balances demand-weighted efficiency with geographic dispersion through Moran′sI regularization; and (3) meta-optimization employing a Genetic Algorithm to calibrate strategic weights WG,WE,WR and maximize NPS. The optimized 15-node portfolio spans 15 provinces, achieving strong spatial equity Moran′sI=0.08, 87% population coverage, and NPS=0.917. Z-number DEA robustly handles deep uncertainty by jointly modeling parameter imprecision A and information reliability B. Geopolitical scenario analysis quantifies systemic impacts ranging from −28% (comprehensive sanctions) to +7% (Caspian expansion). The framework illustrates conceptually cross-national transferability through a structured Egyptian adaptation and establishes quantifiable linkages to nine UN Sustainable Development Goals (SDGs) as emergent outcomes – not design objectives. This work contributes to geo-information science by embedding GIS-based spatial evaluation within an iterative network optimization framework, enabling spatial suitability, operational efficiency, and spatial risk to be jointly integrated into a network-aware Spatial Decision Support System for strategic infrastructure planning under deep uncertainty.