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

• Autorzy: Wyrembek Mateusz

Identyfikatory

DOI

10.17270/J.LOG.2023.837

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

ant colony optimization; machine learning; supply chain risk management; delay prediction

PL

optymalizacja kolonii mrówek; nauczanie maszynowe; zarządzanie ryzykiem w łańcuchu dostaw; przewidywanie opóżnienia

• Wydawca: Wyższa Szkoła Logistyki
• Czasopismo: LogForum
• Rocznik: 2023
• Tom: Vol. 19, no. 2
• Strony: 236--275
• Opis fizyczny: Bibliogr. 29 poz., rys., tab., wykr.

Twórcy

autor

Wyrembek Mateusz

• Department of Logistics, Institute of International Business and Economics, Poznań University of Economics and Business, Poznań, Poland

• https://orcid.org/0000-0002-7946-948X

Bibliografia

• 1. Bartz, E., Bartz-Beielstein, T., Zaefferer, M., Mersmann, O. (Eds.). (2023). Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide. Springer. https://doi.org/10.1007/978-981-19-5170-1
• 2. Baryannis, G., Dani, S., & Antoniou, G. (2019). Predicting supply chain risks. Using machine learning: The trade-off between performance and interpretability. Future Generation Computer Systems, 101, 993–1004. https://doi.org/10.1016/j.future.2019. 07.059
• 3. Biazon de Oliveira, M., Zucchi, G., Lippi, M., Farias, C., Rosa da Silva, N., & Iori, M. (2021). Lead time forecasting with machine learning techniques for a pharmaceutical supply chain. Paper presentation. Proceedings of the 23nd International Conference on Enterprise Information Systems on International Conference on Enterprise Information Systems (ICEIS). 26–28.04.2021. https://www.scitepress.org/ Papers/2021/104344/104344.pdf
• 4. Boryczka, U., & Kozak, J. (2010). Ant colony decision trees—a new method for constructing decision trees based on ant colony optimization. In J.S. Pan, S.M. Chen, & N.T Nguyen (Eds.), Computational Collective Intelligence. Technologies and Applications (pp. 373 - 382). Springer. https://doi.org/10.1007/978-3-642-16693-8_39
• 5. Boryczka, U., & Kozak, J. (2016). Adaptive Ant Clustering Algorithm with Pheromone. In N.T. Nguyen, B. Trawiński, H. Fujita, T.P. Hong (Eds.), Intelligent Information and database Systems (pp. 117-126). Springer. https://doi.org/10.1007/978-3-662-49390-8_11
• 6. Chengsheng, T., Huacheng, L., & Bing, X. (2017). AdaBoost typical Algorithm and its application research. Paper presentation. Proceedings of the 3rdInternational Conference on Mechanical, Electronic and Information Technology Engineering (ICMITE 2017). 16-17.12.2017. https://doi.org/10.1051/matecconf/201713900222
• 7. Colorni, A., Dorigo, M., Maniezzo, V. (1991). Distributed optimization by ant colonies. In F.J. Varela & P. Bourgine (Eds.), Proceedings of European Conference Artificial Life (ECAL ’91) (pp. 134 - 142). Elsevier Publishing.
• 8. Colorni, A., Dorigo, M., Maniezzo, V., Trubian, M. (1994), Ant system for job-shop scheduling, ORBEL Belgian Journal of Operations Research. Statistics and Computer Science, 34, 39-53.
• 9. Dong, G., & Liu, H. (Eds.). (2018). Feature engineering for machine learning and data analytics. CRC Press. https://doi.org/ 10.1201/9781315181080
• 10. Emmanuel, T., Maupong, T., Mpoeleng, D., Semong, T., Banyatsang, M., & Tabona, O. (2021). A survey on missing data in machine learning. Journal of Big data, 8(140). https://doi.org/10.1186/s40537-021-00516-9
• 11. Gao, R., & Liu, Z. (2020). An Improved AdaBoost Algorithm for Hyperparameter Optimization. Journal of Physics: Conference Series, 1631(2020). https://doi.org/10.1088/1742-6596/1631/1/012048
• 12. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
• 13. Hatıpoğlu, I., Tosun, Ö., Tosun, N. (2022). Flight delay prediction based with machine learning. LogForum, 18(1), 97-107. https://doi.org/10.17270/J.LOG.2022.655
• 14. Iwendi, C., Mahboob, K., Khalid, Z., Javed, A. R., Rizwan, M., & Ghosh, U. (2022). Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system. Multimedia systems, 28(4), 1223–1237. https://doi.org/10.1007/s00-530-021-00774-w
• 15. Jadczak, R. (2019). Vehicle routing in supply chain management. Models, methods, and applications. Wydawnictwo Uniwersytetu Łódzkiego.
• 16. Kozak, J., & Boryczka, U. (2016). Collective Data Mining in The Ant Colony Decision Tree Approach. Information Sciences, 372, 126-147. https://doi.org/10.1016/j.ins.2016.08.051
• 17. Kozak, J. (2019). Decision Tree and Ensemble Learning Based on Ant Colony Optimization. Springer. https://doi.org /10.1007/978-3-319-93752-6
• 18. Kuhn, M., Johnson, K. (2016). Applied predictive modeling (5th ed.). Springer.
• 19. Mitchell, T. M. (1997). Machine learning. McGraw-Hill.
• 20. Sarkar, D., Bali, R., & Sharma, T. (2018). Practical Machine Learning with Python. A Problem-Solver’s Guide to Building Real-World Intelligent Systems. Apress. https://doi.org/10.1007/978-1-4842-3207-1
• 21. Socha, K., Blum, C. (2006). Ant Colony Optimization. In E. Alba & R. Martí (Eds.), Metaheuristic Procedures for Training Neural Networks (pp. 153 - 180). Springer. https://doi.org/10.1007/0-387-33416-5_8
• 22. Steinberg, F., Burggräf, P., Wagner, J., & Heinbach, B. (2022). Impact of material data in assembly delay prediction- a machine learning-based case study in machinery industry. The International Journal of Advanced Manufacturing Technology, 120, 1333-1346. https://doi.org/10.1007/s00170-022-08767-3
• 23. Steinberg, F., Burggräf, P., Wagner, J., Heinbach, B., Saßmannshausen, T., Brintrup, A. (2023). A novel machine learning model for predicting late supplier deliveries of low-volume-high-variety products with application in a German machinery industry. Supply Chain Analytics, 1. https://doi.org/10.1016/ j.sca.2023.100003
• 24. Ristoski, P. & Paulheim, H. (2016). Semantic web in data mining and knowledge discovery: A comprehensive survey. Journal of Web Semantics, 36, 1–22. https://doi.org/10.1016/j.websem.2016.01.001
• 25. Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson Education.
• 26. Trajkovski, A., & Madjarow, G. (2022). Model Hyper Parameter Tuning using Ant Colony Optimization. Paper presentation. Proceedings on the 19th Intrnational Conference on Informatics and Information Technologies – CIIT 2022. https://repository.ukim.mk/bitstream/20.500.12188/25676/1/CIIT_2022_7.pdf
• 27. Werner-Lewandowska, K., Koliński, A., & Golinska-Dawson, P. (2023). Barriers to electronic data exchange in the supply chain – result from empirical study. LogForum, 19(1), http://doi.org/10.17270/J.LOG.2023. 804
• 28. Wyrembek, M. (2022). The use of Big Data technology to predict risk of delay in supply chain. Material Economy and Logistics, 6, 29-36. https://doi.org/10.33226/1231-2037.2022.6.4
• 29. Wyrembek, M. (2023). The use of machine learnings methods to predict the risk of damage to goods. Material Economy and Logistics, 1, 58-66, https://doi.org/ 10.33226/1231-2037.2023.1.7