Enhanced vehicle routing for medical waste management via hybrid deep reinforcement learning and optimization algorithms.

Abstract

Modern technologies, particularly artificial intelligence, play a crucial role in improving medical waste management by developing intelligent systems that optimize the shortest routes for waste transport, from its generation to final disposal. Algorithms such as Q-learning and Deep Q Network enhance the efficiency of transport and disposal while reducing environmental pollution risks. In this study, artificial intelligence algorithms were trained using Homogeneous agent systems with a capacity of 3 tons to optimize routes between hospitals within the Closed Capacitated Vehicle Routing Problem framework. Integrating AI with pathfinding techniques, especially the hybrid A*-Deep Q Network approach, led to advanced results despite initial challenges. K-means clustering was used to divide hospitals into zones, allowing agents to navigate the shortest paths using the Deep Q Network. Analysis revealed that the agents' capacity was not fully utilized. This led to the application of Fractional Knapsack dynamic programming with Deep Q Network to maximize capacity utilization while achieving optimal routes. Since the criteria used to compare the algorithms' effectiveness are the number of vehicles and the utilization of the total vehicle capacity, it was found that the Fractional Knapsack with DQN stands out by requiring the fewest number of vehicles (4), achieving 0% loss in this metric as it matches the optimal value. Compared to other algorithms that require 5 or 7 vehicles, it reduces the fleet size by 20 and 42.86%, respectively. Additionally, it maximizes vehicle capacity utilization at 100%, unlike other methods, which utilize only 33 to 66% of vehicle capacity. However, this improvement comes at the cost of a 9% increase in distance, reflecting the longer routes needed to serve more hospitals per trip. Despite this trade-off, the algorithm's ability to minimize fleet size while fully utilizing vehicle capacity makes it the optimal choice in scenarios where these factors are critical. This approach not only improved performance but also enhanced environmental sustainability, making it the most effective and challenging solution among all the algorithms used in the study.