Reinforcement Learning-Driven Enhancement of Medical Waste Collection within Capacity-Homogeneous Vehicle Routing | ||||
| IJCI. International Journal of Computers and Information | ||||
| Article 7, Volume 11, Issue 2, July 2024, Page 79-94 PDF (1.56 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijci.2024.288957.1163 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Norhan Mohamed Khallaf  1; Osama Abdel-Raouf2; Mohy Hadhoud3; ِِAhmed Kafafy4
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| 1Faculty of artificial intelligence, Menofia University | ||||
| 2Operations Research and Decision Support Department, Faculty of Computers and Information, Menoufia University, Menoufia, Egypt | ||||
| 3Department of information technology, Faculty of computers and Information, Menofia University | ||||
| 4Operations Research & DSS Dept., Faculty of Computers and Information, Menoufia University, Menoufia, Egypt | ||||
| Abstract | ||||
| Artificial intelligence is increasingly being used in various fields, including the management of hazardous medical waste. Medical waste poses an economic burden and a risk to public health, and it should be disposed of with care, preferably in areas far from residential areas. Data was collected on waste generated by 15 government hospitals in Menoufia Governorate and a single disposal site in Kafr Dawood, along with a central collection point for waste transport vehicles. This study addresses the issue of limited-capacity vehicle routing, which is considered a complex problem (NP-hard). Specific vehicles are designated to collect waste from hospitals and transport it to the disposal center, with the goal of finding the shortest route while maximizing the vehicle’s capacity, which is limited to three tons. Reinforcement learning techniques were developed, treating the vehicle as an agent trained to choose the shortest, least costly route between hospitals. The SARSA algorithm was implemented and improved. Solutions include SARSA, Dijkstra, knapsack dynamic programming, and hybrid approaches that combine SARSA with Dijkstra and SARSA with knapsack dynamic programming. The result shows that the hybrid approach between SARSA and knapsack dynamic programming is the most effective, as it reduces the number of vehicles used for waste transport and maximizes the vehicle’s capacity, determining the shortest routes between all hospitals. Finally, transportation costs were calculated to complete the mathematical model for medical waste management. | ||||
| Keywords | ||||
| Reinforcement learning; Closed Capacity Vehicle Routing Problem; Dijkstra; knapsack problem | ||||
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