Spatial Input Dataset for Obstacle-Aware Multi-Target Routing in Campus Logistics

Résumé fourni par la source

This dataset provides the spatial input data used in the study “AI-Enabled Decision-Making for Sustainable and Resource-Efficient Campus Logistics: Obstacle-Aware Multi-Target Routing Using an Improved Mayfly Optimization Algorithm.” It includes the synthetic G40 benchmark grid and fixed task-node coordinates, as well as the processed G-real campus grid and fixed task-node coordinates used for application-level validation. The G40 grid is 40 × 40 and the G-real grid is 1100 × 1200. In both grid files, 0 denotes a traversable cell and 1 denotes an obstacle or non-traversable cell. The G40 task nodes were generated once and then kept fixed across all algorithms and runs to ensure consistent comparison. The repository also includes a README file describing the dataset structure and encoding. These data are provided to support transparency and reproducibility of the spatial modeling and routing experiments reported in the associated study.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.