(04) Developing a Humanitarian Logistics Framework Using a Reinforcement Learning Technique, Khalid AlQumaizi, Ashit Dutta, Sultan Alshehri

Khalid I AlQumaizi
Assistant Professor of Family Medicine
College of Medicine
AlMaarefa University, Ad Diriyah, Riyadh, 13713
Kingdom of Saudi Arabia
Email: kqumaizi@mcst.edu.sa
Ashit Kumar Dutta
Department of Computer Science and Information Systems,
College of Applied Sciences
AlMaarefa University, Ad Diriyah, Riyadh, 13713
Kingdom of Saudi Arabia
Email: adotta@mcst.edu.sa
Sultan Alshehri
Department of Pharmaceutical Sciences
College of Pharmacy
Almaarefa University, Ad Diriyah 13713
Kingdom of Saudi Arabia
Email: sshehri.c@mcst.edu.sa

DOI: 10.47556/J.WJEMSD.19.3-4.2023.4
Abstract: Humanitarian logistics (HL) refers to the coordination of relief efforts to ensure that disaster victims have timely access to necessary goods. The primary objectives of HL are life preservation and post-disaster reduction. Disasters like earthquakes and tsunamis necessitate the prompt and adequate delivery of emergency relief supplies. There is a lack of an effective optimization model for allocating resources in HL. Thus, this paper presents a reinforcement learning-based framework for optimizing the resource allocation processes in HL. The authors employ the State Action Reward State Action algorithm to reduce the complexities in resource allocation. In addition, a transportation plan is developed in order to support the victims at remote locations. The suggested algorithm is evaluated against the precise dynamic programming approach and a heuristic algorithm to determine which provides the best quality result. The experimental findings reveal that the algorithm outperforms the state-of-the-art methods regarding efficiency and accuracy. In addition, the Q-learning algorithm can deliver solutions closer to optimum or even ideal by improving the training phase.
Keywords: Humanitarian logistics, Q-learning, SARSA, Emergency relief supplies, Reinforcement learning.