Abstract—In this research study, a novel metaheuristic approach using nanotechnology is proposed, known as Artificial Carbon Nanotube Synthesis Optimization (ACNSO), in order to develop a vaccine cold chain network in north of Thailand. The scope of the study emphasizes Area 1 of the Office Disease Prevention and Control in the Chiang Mai region. Vaccines must be transported both to the Provincial Health Offices and hospitals in the region. This study seeks to arrange the transportation routes involved in order to achieve the shortest possible total distance. The algorithm must first of all assess the travel conditions between each point in the network, and then generate the starting solution. Efficient solutions to this problem will cut the total processing time. The study then made a comparison between the results produced by ACNSO algorithm and those of other algorithms used in earlier studies. Full factorial design was the statistical approach used to evaluate the optimal parameters for the algorithm. The experiment was designed to examine the various factors which influence the algorithm performance. The results showed that ACNSO algorithm found the best solution in experimental algorithms and 3rd processing time.
Index Terms—Vaccine cold chain network, metaheuristic approach, full factorial design, nanotechnology, artificial carbon nanotube synthesis optimization.
Kanon Sujaree is with the Department of Industrial Engineering, Faculty of Engineering, Rajamangala University of Technology Rattanakosin, Nakhonpathom, Thailand (e-mail: kanon.suj@rmutr.ac.th)
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Cite: Kanon Sujaree, "Artificial Carbon Nanotube Synthesis Optimization to Address the Vaccine Cold Chain Network Problem," International Journal of Modeling and Optimization vol. 10, no. 1, pp. 32-40, 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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