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 hosp

SUBMITTER: Khallaf N 

PROVIDER: S-EPMC11861366 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature