The The Optimization of Cassava Transportation to Reduce Carbon Emission

Abstract

This research utilized Genetic Algorithm (GA) to optimize cassava transportation routes from cultivation fields to nearby collection yards, with the objective of minimizing CO₂ emissions based on optimized distances. Data collected included plot coordinates, plot size, vehicle type, vehicle weight, and payload capacity for each vehicle type to collection yard A, as well as coordinates of two alternative nearby collection yards, B and C. Four vehicle types, including tractor, farm tractor, 6-wheel truck, and pickup truck, were analyzed in transporting cassava from 20 surrounding plots to collection yard A, with each delivering different volumes of cassava. The GA-based optimization of routing grouped the 20 plots into efficient collection routes servicing three different yards (A, B, and C), achieving a total of 18 optimized trips, which the results section that the workload was equally distributed, with each yard (A, B, and C) receiving 6 trips to ensure operational consistency across all route and payload identified a required vehicle capacity of 17,000 kg to satisfy the minimum theoretical trip demand. When CO₂ emissions were calculated along the optimized route, the observed emissions from actual operations were substantially higher than the GA-derived values. Total CO₂ emissions GA were decreased carbon emissions by up to 67.2 – 68.1 % compared to using Tractors and Farm tractors, 54.6 % when using 4-wheel pickup trucks and 0.09 % for the 6-wheel trucks. The results demonstrate that GA driven optimization significantly enhances agricultural logistics by reducing transportation time, costs, and environmental impact through the strategic selection of collection yards and vehicle types