Vehicle routing problem optimization with machine learning in imbalanced classification vehicle route data - Novelan, Muhammad Syahputra; Efendi, Syahril; Sihombing, Poltak; Mawengkang, Herman (Eastern-European Journal of Enterprise Technologies, 2023)
The object of this research is a combinatorial optimization problem arising in the problem of the route of goods delivery vehicles. In this study, the proposed method for solving combinatorial optimization problems consists of several stages: Data Cleaning, Data Preprocessing, K-NN and Cavacity Vehicle Routing Problem model. The results show that the machine learning approach can optimise combinatorial optimization problems, especially in generating vehicle route points and delivery capacity. The characteristics in determining vehicle routes by considering latitude and longitude points. This research builds a framework and implements it in a multi-class optimization model to reduce overfitting and misclassification results caused by unbalanced multiclassification from the influence of the number of 'nodes' on vehicle routes with machine learning. The purpose of the model in general is to gain an understanding of the mechanism in the problem so that it can classify unbalanced vehicle route data based on Jalur Nugraha Ekakurir delivery routes. So that with the availability of the model can be a model in determining vehicle routes based on the capacity limit of the number of shipments of goods. The results of research with machine learning models and vehicle routing problems with testing K values 11, 13, 15. Where it has a percentage of K=11 accuracy 57.3265 % and K=13 accuracy 57.3265 % and K=15 accuracy 81.8645 %. From the test results with odd K values have better accuracy and the K 15 K=15 value is better with a percentage of 81.8645 % compared to K 11 K=11, and 13 K=13. As a result, the developed model in terms of accuracy of the cavacity vehicle routing problem model has an accuracy of 93.80 % and the time series achieves an average precision of 93.31 % and with a recall value of 93.80 %. The results obtained can be useful in developing a more modern model, Cavacity Vehicle Routing Problem with Machine Learning