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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.

Evaluation of sustainable supplier problem: a hybrid decision making model based on SWARA-WASPAS

Erdogan Hande, Tosun Nedret

LogForum

2021

Vol. 17, no. 4

465--476

Background: The fierce competitive advantage in the global market depends largely on the integration of all supply chain networks. This network facilitates the movement of information and materials through the suppliers and end customers with a focus on planning and managing. This integration can result in the meeting demands of customer orders being affected by the performance of the suppliers. As a result of this integration, it can be considered that the performance of the suppliers is important in fulfilling customer orders on time. Evaluating and selecting suppliers is greatly influencing the performance of the supply network. Methods: Selecting the proper supplier is a multi-criteria decision-making problem which includes both quantitative and qualitative criteria. A two-stage decision making method is proposed in the study under sustainability dimension. First, SWARA method is used to determine the relative importance of criteria and than WASPAS method is used to evaluate and rank the given alternatives. Results: A real-life case study is given for the selected approach. Also, sensitivity analysis is given. This selected alternative confirms the preferences of decision makers as it is a company that operates internationally and has a reputation and awareness in sustainability within its own country. Conclusions: Due to the increase in awareness on sustainability and the resulting regulations, the issue of sustainability in supply chains and sustainable supplier selection has become an important issue for companies. It is aimed to examine the supplier selection of a company in an electronics sector on a "sustainable" basis, considering from economic, environmental and social aspects. In this study, which was carried out to fill the literature gap identified in this field and to propose a systematic approach to sustainable supplier selection, a hybrid method which consists of both SWARA and WASPAS method have been used to evaluate the suppliers under the sustainability dimensions. With the help of a hybrid model, decision makers can manage conflict management of individual challenges using an analytical process.