A decision-making system for medical transportation mode using machine learning methods

Abstract

There is number of complicated operations in freight transportation system to cover customer demands in the world. Nowadays, companies have huge competition to fulfill customer needs and get a higher level of performance in freight transportation. Transportation mode has been considered as one of the components that has influence on service levels of freight transportation. Road, sea, air are popular modes of transportation which have different features and unique benefits. They also have various costs, different emissions in environment and risks in society. People use these transportation modes based on their needs, but they do have some advantages and disadvantages. When we are dealing with a lot of shipment transactions in a company, making decision for choosing the best option will not be easy. Companies face with challenges since there are numerous factors effecting shipment mode selection. Moreover, the number of low-volume and high-frequency shipments has also increased due to increased demand diversity, shorter product life cycles, and increased agile customer response. Consequently, logistics costs are increasing for those shippers who need to export a small number of products abroad. As a result, researchers have been actively focusing on this matter, which has a significant impact on a country's social and economic situation. This research aims to develop a hybrid approach to create a shipment selection model with a case study of pharmaceutical drugs by machine learning algorithms, checking the accuracy of predictions and using multi-criteria decision-making methods (MCDM) for validation of our work. Several different features of the dataset including shipping cost, country of origin and destination, cargo weight, cargo dimensions, etc., are given to decision tree, Random forest, logistic regression, XGboost and SVM machine learning algorithms so that we can predict the best shipping method by land, air, or sea. Then, using different criteria F1 score, Recall and precision, accuracy score we measured the accuracy of the forecast and finally, we validated the research method by MCDM methods SAW, MARCOS, TOPSIS, MULTIMOORA and VIKOR. After being familiar with all important factors, tools, research gap in literature review, we realized that choosing one machine algorithm is not enough to get an accurate result and we used the most popular ones. data science scored important features influencing transportation modes and used machine learning techniques to learn the factors and the relationships between them to increase the accuracy of the pharmaceuticals drugs shipment selection system. By MCDM we found XGboost as the best machine learning algorithm to predict the shipment mode with the average performance evaluation of 84 percentage then random forest, decision tree, SVM and LR respectively.