OPTİMİZASYON VE YAPAY ZEKÂ ALGORİTMALARI KULLANARAK MENÜ PLANLAMA YAZILIMI GELİŞTİRİLMESİ

Öz

Günümüzde sağlık ve beslenme bilinci giderek artmakta, bu da kişiye özel beslenme önerilerinin ve gıda israfının azaltılmasının önemini vurgulamaktadır. Bu çalışmada, kullanıcılardan demografik veriler ve gıda tercihleri toplanarak yapay zeka modelleri ile analiz edilmiştir. Random Forest algoritması kullanılarak geliştirilen bir model, kullanıcıların gelecekteki tercihlerini tahmin etmek ve menü planlamasına rehberlik etmek üzere eğitilmiştir. Yapılan testler, yapay zeka ve optimizasyon tekniklerinin birleştirilmesinin kullanıcı odaklı menüler oluşturduğunu, memnuniyeti artırdığını ve gıda israfını azalttığını göstermiştir. Çalışma ayrıca, veri setinin boyutuyla ilgili zorluklara dikkat çekerek, daha nitelikli verilere olan ihtiyacı ortaya koymuştur. Geliştirilen model, toplu yemek hizmeti sunan catering şirketleri ve diğer kurumlar için yenilikçi çözümler sunarak çalışan memnuniyetini artırırken israfı da minimize etmektedir. Gelecek araştırmalar, modelin daha geniş uygulamalar için geliştirilmesini hedeflemektedir.

Anahtar Kelimeler

• Artificial Intelligence
• Optimization Algorithms
• Linear Programming
• Personalized Menu
• Catering

DEVELOPING MENU PLANNING SOFTWARE USING OPTIMIZATION AND ARTIFICIAL INTELLIGENCE ALGORITHM

Öz

In today's world, awareness of health and nutrition is growing, emphasizing the need for personalized nutrition recommendations and reducing food waste. This study collected demographic data and food preferences from users and analyzed them using artificial intelligence models. A model developed with the Random Forest algorithm was trained to predict users' future preferences and guide menu planning. Tests showed that combining AI with optimization techniques successfully creates user-focused menus, enhancing satisfaction and reducing food waste. The study also highlighted challenges related to the dataset's size, pointing to a need for more qualitative data. The developed model provides innovative solutions for catering companies and institutions offering mass dining, improving employee satisfaction while minimizing waste. Future research aims to refine the model for broader applications.

Anahtar Kelimeler

• Artificial Intelligence
• Optimization Algorithms
• Linear Programming
• Personalized Menu
• Catering

Kaynakça

1. Ahmed, F., Kim, K.Y. (2017). Data-driven weld nugget width prediction with decision tree algorithm. Procedia Manufacturing,10, 1009–1019, https://doi.org/10.1016/j.promfg.2017.07.092
2. Balcan, M.-F., Prasad, S., Sandholm, T., Vitercik, E. (2022). Structural analysis of branch-and-cut and the learnability of gomory mixed integer cuts. Advances in Neural Information Processing Systems, 35, 33890-33903, https://doi.org/ 10.48550/arXiv.2204.07312
3. Basu, A., Conforti, M., Di Summa, M., Jiang, H. (2023). Complexity of branch-and-bound and cutting planes in mixed-integer optimization. Mathematical Programming, 198, 787–810, https://doi.org/10.1007/s10107-022-01789-5
4. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/ 10.1023/A:1010933404324 Chandru, V., Rao, M. (1998). Linear programming. IIM Bangalore Research Paper, (109), https://doi.org/10.2139/ssrn.2170298
5. Kulhari, A. (2023). Significance of Linear Programming for Optimization. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), 3(15), 179-183. https://doi.org/10.48175/IJARSCT-10899
6. Dantzig, G. B. (1982). Reminiscences about the origins of linear programming. Operations Research Letters, 1(2), 43-48. https://doi.org/10.1016/ 0167-6377(82)90043-8
7. Gong, X. (2022). Optimization algorithm of logistics warehousing and distribution path based on artificial intelligence technology. In 2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) (pp. 371-375).. https://doi.org/10.1109/ISAIEE57420.2022.00083
8. Hebbar, N. (2020). Freshness of food detection using IoT and machine learning. In 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) (pp. 1-3). https://doi.org/10.1109/ic-ETITE47903.2020.80

9. Hou, S., Zhu, D., Xu, J. (2022). Artificial intelligence, financial canteen and internal control: A case study of Chinese catering industry. In 2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI) (pp. 310-313). https://doi.org/10.1109/ IWECAI55315.2022.00066
10. Liaw, A., Wiener, M. (2002). Classification and Regression by randomForest. R News, 2(3), 18-22.
11. Li, Y., Yan, H., Zhang, Y. (2019). A deep learning method for material performance recognition in laser additive manufacturing. In 2019 IEEE 17th International Conference on Industrial Informatics (INDIN) (pp. 1735-1740). https://doi.org/ 10.1109/INDIN41052.2019.8972334
12. Miltenberger, M. (2023). Linear Programming in MILP Solving a Computational Perspective. https://nbn-resolving.org/urn:nbn:de:0297-zib-91873
13. Misra, N. N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R., Martynenko, A. (2022). IoT, big data, and artificial intelligence in agriculture and food industry. IEEE Internet of Things Journal, 9(9), 6305-6324. https://doi.org/10.1109/ JIOT.2020.2998584
14. Nasteski, V. (2017). An overview of the supervised machine learning methods. Horizons.B, 4, 51-62. https://doi.org/10.20544/ HORIZONS.B.04.1.17.P05
15. Precup, R.-E., Hedrea, E.-L., Roman, R.-C., Petriu, E. M., Szedlak-Stinean, A.-I., Bojan-Dragos, C.-A. (2021). Experiment-based approach to teach optimization techniques. IEEE Transactions on Education, 64(2), 88-94. https://doi.org/10.1109/TE.2020.3008878
16. Russell, S., Norvig, P. (2003). Artificial intelligence - a modern approach, 2nd Edition. Prentice Hall series in artificial intelligence.
17. Sharma, A., Jain, A., Gupta, P., Chowdary, V. (2021). Machine learning applications for precision agriculture: A comprehensive review. IEEE Access, 9, 4843-4873. https://doi.org/ 10.1109/ACCESS.2020.3048415
18. Tiwari, P., Agrawal, D. (2022). A study of linear programming technique. International Journal of Statistics and Applied Mathematics, 7(2), 54-56. https://doi.org/10.13140/RG.2.2.26343.52641