Optimising Logistics Operations using Geospatial Algorithms with Adaptive Route Evolution
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Abstract
Rapid urbanisation in cities like Lagos, Nigeria has impaired traffic congestion and route-planning, causing delivery delays stemming from logistic inefficiencies leading to operational costs; to overcome these challenges, an Adaptive Route Evolution (ARE) model that integrates A* for initial shortest-path generation, a Genetic Algorithm (GA) for offline route refinement, and Reinforcement Learning (RL) for real-time adjustments based on live traffic data is proposed. In simulations of two vehicles traversing standard delivery corridors with geospatial road-network data and real-time traffic APIs, Vehicle 1’s delivery time decreased from 78 min to 70 min after A* (–10.3%), 65 min after GA (–17%), and 61 min after RL (–21.8%), while Vehicle 2’s time fell from 120 min to 90 min after A* (–25%), 83 min after GA (–30.8%), and 79 min after RL (–34.2%), yielding an average 27.5% reduction in delivery time and an estimated 22% drop in fuel and driver costs. ARE provided a complete end-to-end optimisation framework by combining RL’s adaptability with A*’s shortest-path planning and GA’s sequence optimisation. It demonstrated scalability and effectiveness in dynamically optimising urban logistics, reducing both transit delays and carbon emissions, and enabling a more sustainable and cost-efficient transportation system in line with Sustainable Development Goals (SDGs) 7, 11, and 13.
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