Accelerating urbanization in Khartoum State has placed considerable pressure on solid waste management (SWM) infrastructure. Inadequate landfill site selection and inefficient collection planning have created environmental risks, economic inefficiency, and public health concerns. This study proposes an integrated GIS-based decision-support framework combining Multi-Criteria Decision Analysis (MCDA) and the Analytic Hierarchy Process (AHP) to improve waste management in Khartoum, Omdurman, and Khartoum North. Seven spatial criteria were weighted using AHP and integrated in ArcGIS to produce a three-class suitability map. Location-Allocation analysis identified three suitable sites, confirming the suitability of the existing Khartoum North site and designating the other two as transfer stations. Service Area Analysis assessed accessibility, while container locations and capacities were optimized according to household walking distances (maximum 250 m) and building density. Python simulations compared two collection scenarios for February 2026: a fixed daily schedule and a demand-driven schedule triggered when containers reached 80% capacity. The demand-driven approach reduced total travel distance by 19.4%, working hours by 44%, and operational costs by 19%. The proposed framework provides a transparent, reproducible, and cost-effective approach to urban solid waste management, particularly in resource-constrained settings.

Routing a medical-delivery UAV to multiple hospitals must jointly account for distance, flight time, payload, and clinical priority. We present a reproducible GIS-based framework for multi-stop UAV emergency medical routing in Khartoum State, Sudan, using a common obstacle-aware 3-D leg model with grid-based A* routing around no-fly zones and payload-dependent climb, cruise, and descent. We show that a linear priority term in a composite tour cost is order-invariant and cannot distinguish visiting sequences. We therefore use a priority-weighted completion-time objective with hard deadlines for Critical hospitals, with criterion weights derived from health professionals using the Analytic Hierarchy Process. On a 14-hospital network, exact Held–Karp and label-setting dynamic programming provide proven optima for benchmarking classical, metaheuristic, and Advantage Actor–Critic sequencing methods (A2C-SP). The revised objective reduces optimal priority-weighted completion time by 14% with only a 0.2% increase in distance. A2C-SP is 14.7% above the optimum across 47 unseen instances while generating routes in approximately 2 ms. Without retraining, it also plans capacity- and range-feasible multi-sortie missions on a 59-hospital city network, achieving a priority-weighted completion time of 110 min versus 137 min for the published toolbox route. At city scale, a single UAV cannot serve more than 5 of 16 Critical hospitals within 15 min, highlighting fleet sizing as a key constraint for meeting such deadlines.