Jessi Febria1, Christine Dewi1,2, Evangs Mailoa1*

1 Faculty of Information Technology, Satya Wacana Christian University, Central Java, Indonesia
2 Department of Information Management, Chaoyang University of Technology, Taiwan, ROC


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Vaccines are the solution that is currently underway to tackle COVID-19. In this paper, vaccine distribution for hospitals in Central Java is developed. The problem case in this paper is classified as a Capacitated Vehicle Routing Problem (CVRP). The method proposed is using an initial route that follows the cluster-first route-second method (CFRS). The same size K-means is used for the clustering phase and the Greedy algorithm is used for the routing phase. The result of the initial route is a clustered route for each vehicle with a balanced capacity. Then, each cluster was re-optimized using metaheuristics Guided Local Search from Google OR-tools. Our experiment results have proven that using the initial route has the effect of reducing runtime by 97.37% - 99.17% when compared to without the initial route. This is because using initial routes with the same size K-means means breaking the problem into parts, then using the Greedy algorithm can reduce the number of possible routes. However, the total distance increased by 8.22% - 16.69% because no cluster member is allowed to move to another cluster.

Keywords: Capacitated vehicle routing problem, Vaccine distribution, K-means.

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Received: 2021-04-20
Revised: 2021-08-10
Accepted: 2021-09-07
Publication Date: 2021-12-01

Cite this article:

Febria, J., Dewi, C., Mailoa, E. Optimization of capacitated vehicle routing problem using initial route with same size K-means and greedy algorithm for vaccines distribution. International Journal of Applied Science and Engineering, 18, 2021103.

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