Iterative Score Propagation Algorithm (ISPA): A GNN-Inspired Framework for Multi-Criteria Route Design with Engineering Applications
Hüseyin Pehlivan · Preprints.org · 2025
Traditional route optimization frameworks often suffer from "spatial blindness," ad-dressing the problem through abstract matrices devoid of geographical context, which yields suboptimal solutions in complex landscapes. To address this fundamental methodological gap, this study proposes the Iterative Score Propagation Algorithm (ISPA), a transparent, GNN-inspired framework that reframes optimization from a set of isolated points to a holistic corridor problem. The robustness and superiority of ISPA were rigorously tested against established Multi-Criteria Decision-Making (MCDM) methods (WLC, TOPSIS, VIKOR). This comparative analysis was conducted across three diverse engineering scenarios—ranging from balanced engineering (rural high-way) and cost-centric optimization (pipeline) to experience maximization (trekking trail)—and under two distinct weighting philosophies (objective Entropy and subjec-tive AHP). The holistic analysis reveals that ISPA achieves the highest final score (0.815) across all six test conditions, demonstrating both the highest overall mean per-formance and the greatest stability. Furthermore, its flexible cost function successfully modeled unconventional objectives, such as a "climbing reward," showcasing a para-digm shift from cost minimization to experience maximization. We conclude that ISPA's superior performance stems from its structural advantage in contextualizing spatial data, rather than a dependency on any specific weighting scheme. Conse-quently, this work introduces not just a novel algorithm but a new, spatially-aware approach that transforms route planning from a static calculation into a dynamic de-sign and scenario analysis tool for planners and engineers.