Development and Improvement of Optimal Solution Finding Efficiency Using the Grey Wolf Optimization: In Case Study of Waste Collection Route Planning
DOI:
https://doi.org/10.53848/jlsco.v12i2.291344Keywords:
Meta-Heuristic, Development and Improvement, Grey Wolf Optimizer (GWO), Vehicle Routing Problem, Waste collection route planningAbstract
From the problem of inefficient waste collection routing operations due to starting from the farthest points, leading to increased total travel distance and fuel consumption. This research aims to develop and enhance the solution-finding efficiency of the Grey Wolf Optimizer (GWO) for waste collection vehicle routing, formulated as a Capacitated Vehicle Routing Problem (CVRP). The study employs waste collection data from three case study villages: Ban Pa Ha, Ban San Ton Kham, and Ban Daowadueng, comprising 52 nodes. The proposed improved GWO the two-group GWO (GWO2) and the three-group GWO (GWO3), are compared against the conventional routing method, the original GWO, Differential Evolution Optimization (DEO), and Particle Swarm Optimization (PSO). The experiments were conducted using the same number of waste collection rounds, set at four. The results showed that the traditional method produced a total distance of 72,200 m., while DEO and PSO yielded average distances of 62,970 and 62,550 m., respectively. The original GWO reduced the distance to 61,150 m., GWO2 further reduced it to 60,250 m., and GWO3 achieved the shortest distance of 58,350 m. or 19.183% compared to the traditional method. Moreover, GWO3 yielded the lowest standard deviation, indicating high consistency and stability in its results. Based on the findings, GWO3 demonstrates the fastest and most efficient convergence among all compared algorithms. Therefore, it is highly suitable for application in waste collection route planning.
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