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The application of adaboost.m1 based on ant colony optimization to classify the risk of delay in the pharmaceutical supply chain

Wyrembek Mateusz

LogForum

2023

Vol. 19, no. 2

236--275

Background: The purpose of this article is to present the developed AdaBoost.M1 based on Ant Colony Optimization (hereby referred to as ACOBoost.M1 throughout the study) to classify the risk of delay in the pharmaceutical supply chain. This study investigates one research hypothesis, namely, that the ACOBoost.M1 can be used to predict the risk of delay in the supply chain and is characterized by a high prediction performance. Methods: We developed a machine learning algorithm based on Ant Colony Optimization (ACO). The meta-heuristic algorithm ACO is used to find the best hyperparameters for AdaBoost.M1 to classify the risk of delay in the pharmaceutical supply chain. The study used a dataset from 4PL logistics service provider. Results: The results indicate that ACOBoost.M1 may predict the risk of delay in the supply chain and is characterized by a high prediction performance. Conclusions: The present findings highlight the significance of applying machine learning algorithms, such as the AdaBoost.M1 model with Ant Colony Optimization for hyperparameter tuning, to manage the risk of delays in the pharmaceutical supply chain. These findings not only showcase the potential for machine learning in enhancing supply chain efficiency and robustness but also set the stage for future research. Further exploration could include investigating other optimization techniques, machine learning models, and their applications across various industries and sectors.

Flight delay prediction based with machine learning

Hatıpoğlu Irmak, Tosun Ömür, Tosun Nedret

LogForum

2022

Vol. 18, no. 1

97--107

Background: The delay of a planned flight causes many undesirable situations such as cost, customer satisfaction, environmental pollution. There is only one way to prevent these problems before they occur, and that is to know which flights will be delayed. The aim of this study is to predict delayed flights. For this, the use of machine learning techniques, which have become widespread with the development of computer capacities and data storage systems, is preferred. Methods: Estimations are made with three up-to-date techniques XGBoost, LightGBM, and CatBoost techniques based on Gradient Boosting from machine learning techniques. The bayesian technique is used for hyper-parameter settings. In addition, the Synthetic Minority Over-Sampling Technique (SMOTE) technique is also used, as the majority of flights are on time and delayed flights, which constitute a minority class, may adversely affect the results. The results are analyzed and shared with and without SMOTE. Results: As a consequence of the application, which was run on a data set containing all of an international airline's flights [18148 flights] for a year, it was discovered that flights may be predicted with high accuracy. Conclusions: The application of machine learning techniques to anticipate flight delays is new, but it has a lot of potential. Companies will be able to avert problems before they develop if delays are correctly estimated, which can generate plenty of issues. As a result, concrete advantages such as lower costs and higher customer satisfaction will emerge. Improvements will be made at the most vulnerable place in the aviation business.