Modified K-Nearest Neighborhood Algorithm For Optimal Selection Of Distribution Centre In The Disaster Relief Operation
Disaster relief operation is refer to an activity where people assist the disaster victim to recover. Inefficiency of distribution centre selection in disaster relief operation makes difficulty for volunteer to perform their humanitarian task. Thus, a strategic location choose of operation centre is...
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| Summary: | Disaster relief operation is refer to an activity where people assist the disaster victim to recover. Inefficiency of distribution centre selection in disaster relief operation makes difficulty for volunteer to perform their humanitarian task. Thus, a strategic location choose of operation centre is being a concern. It has been pointed out that not all disaster area can be covered during disaster recovery operation . The problem of the selection of distribution centre is not done optimally. The methodology by comparison between K-means, K-means with Simulated Annealing (SA), lastly K-means with Genetic Algorithm (GA). In response to the problems, it is needed to understand the existing algorithm used to determine the distribution centre. The K-Nearest Neighbor (KNN) and the use of Genetic Algorithm (GA) and Simulated Annealing (SA) in KNN is proposed to classify and select the distribution centre. The minimization of the fitness value is being the objective in this study. The experiment conducted with demand point and the distribution centre are located by researcher and complement the KNN with the GA and SA. The comparison of the performance has been made and the study found that implementing GA-KNN give the most optimal solution with average 21% of fitness value least compared to SA-KNN. Thus, this study is contributing in finding the most optimal distribution centre location with nearly-equal demand point distribution of each selected location which can facilitates the real-world aid distribution in disaster area, time-wise and cost-wise. |
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