<![CDATA[Background: Inefficient waste collection practices in urban areas lead to excessive vehicle trips, increased fuel consumption, and unnecessary greenhouse gas emissions. Landfill operations face challenges in route planning because deploying IoT sensors across large and harsh environments is often impractical. Objective: This study proposes a field-data-integrated vehicle routing optimization framework for landfill waste collection that combines greedy allocation, the nearest neighbor heuristic (NNH), and 2-opt local search to minimize travel distance and fuel consumption. Methods: A priority-based capacitated vehicle routing problem (CVRP) model was developed using fill level, waste tonnage, and proximity to the depot as weighted scoring factors within a Design Science Research (DSR) framework, using operational data from 15 landfill drop points in Cakung Barat, East Jakarta. Results: The optimized approach reduced total travel distance by 7.0% and fuel consumption by 7.5% compared with the sequential baseline, while maintaining full service coverage and identical vehicle utilization at 73.2%. A paired t-test confirmed the statistical significance of the improvement (p = 0.038). Conclusion: Field-reported operational data, integrated with multifactor priority scoring and spatial optimization heuristics, provides a practical, cost-effective, and scalable alternative to sensor-based monitoring for landfill waste collection routing.]]>
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