Connecting Cities: Optimal Resource Distribution Problem by Critical Range Radius

Navigating and planning optimal paths for resource delivery algorithms presents significant physical and technical challenges in urban areas, particularly due to the use of existing infrastructure. As smart cities continue to grow, the importance of these algorithms becomes increasingly evident. The current urban landscapes are becoming saturated, which increases the complexity and difficulty of navigating vital resources. However, navigating densely connected networks can be intricate, often requiring substantial computational resources or additional algorithms. Unfortunately, there is a scarcity of explicit algorithms for navigating these networks, leading to a reliance on heuristic approaches and previous network systems. This dependence can create computational challenges, as navigation in this context often involves a combinatorial search space. One recent solution to address this issue is Morphological Shortness Path Planning (MSPP), which offers an efficient method for calculating the best trajectory within a complex graph. In larger towns, calculating and estimating the optimal trajectory and delivery time for resources starting from a store is a common task. Various external factors, such as average speed, time, and distance, influence these challenges. This paper presents a strategy for computing and forecasting delivery times by analyzing the accessibility of reliable paths from a delivery center. The results demonstrate a more efficient response time, ultimately improving planning for resource delivery in complex urban environments.