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Year 2024, Volume: 4 Issue: 1, 43 - 56, 01.05.2024

Elmas Yalçinkaya Ouranıa Areta Hızıroğlu

Abstract

References

  • [1] Zhao, Y., & Bacao, F. (2020). What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period? International Journal of Hospitality Management, 91, 102683. https://doi.org/10.1016/j.ijhm.2020.102683
  • [2] Zhao, B., Tan, H., Zhou, C., & Feng, H. (2023). Optimal delivery time and subsidy for IT-enabled food delivery platforms considering negative externality and social welfare. Industrial Management & Data Systems, 123(5), 1336–1358. https://doi.org/10.1108/IMDS-09-2022-0554
  • [3] Saad, A. T. (2020). Factors affecting online food delivery service in Bangladesh: an empirical study. British Food Journal, 123(2), 535–550. https://doi.org/10.1108/BFJ-05-2020-0449
  • [4] Chu, H., Zhang, W., Bai, P., & Chen, Y. (2023). Data-driven optimization for last-mile delivery. Complex Intell. Syst., 9(3), 2271–2284. https://doi.org/10.1007/s40747-021-00293-1
  • [5] Zhuang, X., Lin, L., Zhang, R., Li, J. (Justin), & He, B. (2021). E-service quality perceptions of millennials and non-millennials on O2O delivery applications. British Food Journal, 123(12), 4116–4134. https://doi.org/10.1108/BFJ-01-2021-0049
  • [6] Yaiprasert, C., & Hidayanto, A. N. (2023). AI-driven ensemble three machine learning to enhance digital marketing strategies in the food delivery business. Intelligent Systems with Applications, 18, 200235. https://doi.org/10.1016/j.iswa.2023.200235
  • [7] Liu, S., He, L., & Shen, Z.-J. M. (2021). On-Time Last-Mile Delivery: Order Assignment with Travel-Time Predictors. Management Science, 67(7), 4095–4119. https://doi.org/10.1287/mnsc.2020.3741
  • [8] Zhang, M.-X., Wu, J.-Y., Wu, X., & Zheng, Y.-J. (2022). Hybrid evolutionary optimization for takeaway order selection and delivery path planning utilizing habit data. Complex Intell. Syst., 8(6), 4425–4440. https://doi.org/10.1007/s40747-021-00410-0
  • [9] Madani, B., & Alshraideh, H. (2021). Predicting Consumer Purchasing Decisions in the Online Food Delivery Industry. In Advances in Machine Learning (pp. 103–117). Academy and Industry Research Collaboration Center (AIRCC). https://doi.org/10.5121/csit.2021.111510
  • [10] Maulud, D., & Abdulazeez, A. M. (2020). A Review on Linear Regression Comprehensive in Machine Learning. Journal of Applied Science and Technology Trends, 1(4), Article 4. https://doi.org/10.38094/jastt1457
  • [11] Moghe, R. P., Rathee, S., Nayak, B., & Adusumilli, K. M. (2021, January 2). Machine Learning based Batching Prediction System for Food Delivery. Proceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD). https://doi.org/10.1145/3430984.3430999
  • [12] Hildebrandt, F. D., & Ulmer, M. W. (2022, July). Supervised Learning for Arrival Time Estimations in Restaurant Meal Delivery. Transportation Science, 56(4), 1058–1084. https://doi.org/10.1287/trsc.2021.1095
  • [13] Zhu, L., Yu, W., Zhou, K., Wang, X., Feng, W., Wang, P., Chen, N., & Lee, P. (2020, August 20). Order Fulfillment Cycle Time Estimation for On-Demand Food Delivery. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3394486.3403307
  • [14] Gao, C., Zhang, F., Wu, G., Hu, Q., Ru, Q., Hao, J., He, R., & Sun, Z. (2021, August 14). A Deep Learning Method for Route and Time Prediction in Food Delivery Service. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3447548.3467068
  • [15] Liu, S., He, L., & Max Shen, Z. J. (2021, July). On-Time Last-Mile Delivery: Order Assignment with Travel-Time Predictors. Management Science, 67(7), 4095–4119. https://doi.org/10.1287/mnsc.2020.3741
  • [16] Gao, C., Zhang, F., Zhou, Y., Feng, R., Ru, Q., Bian, K., He, R., & Sun, Z. (2022, August 14). Applying Deep Learning Based Probabilistic Forecasting to Food Preparation Time for On-Demand Delivery Service. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3534678.3539035
  • [17] Hughes, S., Moreno, S., Yushimito, W. F., & Huerta-Cánepa, G. (2019, December). Evaluation of machine learning methodologies to predict stop delivery times from GPS data. Transportation Research Part C: Emerging Technologies, 109, 289–304. https://doi.org/10.1016/j.trc.2019.10.018
  • [18] Food Delivery Dataset. (2022, October 1). Kaggle. https://www.kaggle.com/datasets/gauravmalik26/food-delivery-dataset/code
  • [19] Murdoch, W. J., Singh, C. D., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences, 116(44), 22071-22080. https://doi.org/10.1073/pnas.1900654116
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  • [22] Yang, N., Korfiatis, N., Zissis, D., & Spanaki, K. (2023). Incorporating topic membership in review rating prediction from unstructured data: a gradient boosting approach. Annals of Operations Research. https://doi.org/10.1007/s10479-023-05336-z
  • [23] Larose, D. T., & Larose, C. D. (2014). Discovering knowledge in data: an introduction to data mining. John Wiley & Sons
  • [24] Pedregosa et al., (2012). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
  • [25] Lin, W., Liu, L., Zhao, G., & Jian, Z. (2023). Developing Hybrid DMO-XGBoost and DMO-RF Models for Estimating the Elastic Modulus of Rock. Mathematics, 11(18), Article 3886. https://doi.org/10.3390/math11183886
  • [26] Wang, X., Li, W., & Li, Q. (2021). A New Embedded Estimation Model for Soil Temperature Prediction. Scientific Programming, 2021, Article 5881018. https://doi.org/10.1155/2021/5881018
  • [27] Shahani, N. M., Kamran, M., Zheng, X., & Liu, C. (2022). Predictive modeling of drilling rate index using machine learning approaches: LSTM, simple RNN, and RFA. Petroleum Science and Technology, 40(5), 534–555. https://doi.org/10.1080/10916466.2021.2003386
  • [28] İnaç, H., Ayözen, Y. E., Atalan, A., & Dönmez, C. Ç. (2022). Estimation of Postal Service Delivery Time and Energy Cost with E-Scooter by Machine Learning Algorithms. Applied Sciences, 12(23), Article 12266. https://doi.org/10.3390/app122312266
  • [29] Ceylan, Z. (2021). The impact of COVID-19 on the electricity demand: a case study for Turkey. International Journal of Energy Research, 45(9), 13022–13039. https://doi.org/10.1002/er.6631

A Comparative Analysis of Machine Learning Models for Time Prediction in Food Delivery Operations

Year 2024, Volume: 4 Issue: 1, 43 - 56, 01.05.2024

Elmas Yalçinkaya Ouranıa Areta Hızıroğlu

Abstract

Accurate time estimation is crucial for ensuring customer satisfaction and operational efficiency in the growing food delivery sector. This paper focuses on comprehensively analyzing factors affecting food delivery times and assessing the effectiveness of machine learning models in forecasting delivery times. For this purpose, authors incorporated a detailed dataset from a food delivery company of the Kaggle platform, encompassing delivery address, order time, delivery time, weather conditions, traffic intensity, and delivery person's profile information. The study evaluated the effectiveness and performance of various machine learning models such as Linear Regression, Decision Trees, Random Forests, and particularly XGBRegressor, using metrics like MAE, RMSE, and R². The results demonstrate that ensemble methods— XGBRegressor—outperformed models in accurately predicting delivery times. Additionally, a thorough analysis of feature importance uncovered the factors influencing delivery time estimation. This study offers insights into leveraging machine learning techniques to optimize food delivery operations and enhance customer satisfaction. The discoveries can assist food delivery platforms in deploying effective time estimation models and emphasizing factors for predictions

Keywords

Machine Learning Time Estimation Feature Importance Food Delivery

References

  • [1] Zhao, Y., & Bacao, F. (2020). What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period? International Journal of Hospitality Management, 91, 102683. https://doi.org/10.1016/j.ijhm.2020.102683
  • [2] Zhao, B., Tan, H., Zhou, C., & Feng, H. (2023). Optimal delivery time and subsidy for IT-enabled food delivery platforms considering negative externality and social welfare. Industrial Management & Data Systems, 123(5), 1336–1358. https://doi.org/10.1108/IMDS-09-2022-0554
  • [3] Saad, A. T. (2020). Factors affecting online food delivery service in Bangladesh: an empirical study. British Food Journal, 123(2), 535–550. https://doi.org/10.1108/BFJ-05-2020-0449
  • [4] Chu, H., Zhang, W., Bai, P., & Chen, Y. (2023). Data-driven optimization for last-mile delivery. Complex Intell. Syst., 9(3), 2271–2284. https://doi.org/10.1007/s40747-021-00293-1
  • [5] Zhuang, X., Lin, L., Zhang, R., Li, J. (Justin), & He, B. (2021). E-service quality perceptions of millennials and non-millennials on O2O delivery applications. British Food Journal, 123(12), 4116–4134. https://doi.org/10.1108/BFJ-01-2021-0049
  • [6] Yaiprasert, C., & Hidayanto, A. N. (2023). AI-driven ensemble three machine learning to enhance digital marketing strategies in the food delivery business. Intelligent Systems with Applications, 18, 200235. https://doi.org/10.1016/j.iswa.2023.200235
  • [7] Liu, S., He, L., & Shen, Z.-J. M. (2021). On-Time Last-Mile Delivery: Order Assignment with Travel-Time Predictors. Management Science, 67(7), 4095–4119. https://doi.org/10.1287/mnsc.2020.3741
  • [8] Zhang, M.-X., Wu, J.-Y., Wu, X., & Zheng, Y.-J. (2022). Hybrid evolutionary optimization for takeaway order selection and delivery path planning utilizing habit data. Complex Intell. Syst., 8(6), 4425–4440. https://doi.org/10.1007/s40747-021-00410-0
  • [9] Madani, B., & Alshraideh, H. (2021). Predicting Consumer Purchasing Decisions in the Online Food Delivery Industry. In Advances in Machine Learning (pp. 103–117). Academy and Industry Research Collaboration Center (AIRCC). https://doi.org/10.5121/csit.2021.111510
  • [10] Maulud, D., & Abdulazeez, A. M. (2020). A Review on Linear Regression Comprehensive in Machine Learning. Journal of Applied Science and Technology Trends, 1(4), Article 4. https://doi.org/10.38094/jastt1457
  • [11] Moghe, R. P., Rathee, S., Nayak, B., & Adusumilli, K. M. (2021, January 2). Machine Learning based Batching Prediction System for Food Delivery. Proceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD). https://doi.org/10.1145/3430984.3430999
  • [12] Hildebrandt, F. D., & Ulmer, M. W. (2022, July). Supervised Learning for Arrival Time Estimations in Restaurant Meal Delivery. Transportation Science, 56(4), 1058–1084. https://doi.org/10.1287/trsc.2021.1095
  • [13] Zhu, L., Yu, W., Zhou, K., Wang, X., Feng, W., Wang, P., Chen, N., & Lee, P. (2020, August 20). Order Fulfillment Cycle Time Estimation for On-Demand Food Delivery. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3394486.3403307
  • [14] Gao, C., Zhang, F., Wu, G., Hu, Q., Ru, Q., Hao, J., He, R., & Sun, Z. (2021, August 14). A Deep Learning Method for Route and Time Prediction in Food Delivery Service. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3447548.3467068
  • [15] Liu, S., He, L., & Max Shen, Z. J. (2021, July). On-Time Last-Mile Delivery: Order Assignment with Travel-Time Predictors. Management Science, 67(7), 4095–4119. https://doi.org/10.1287/mnsc.2020.3741
  • [16] Gao, C., Zhang, F., Zhou, Y., Feng, R., Ru, Q., Bian, K., He, R., & Sun, Z. (2022, August 14). Applying Deep Learning Based Probabilistic Forecasting to Food Preparation Time for On-Demand Delivery Service. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3534678.3539035
  • [17] Hughes, S., Moreno, S., Yushimito, W. F., & Huerta-Cánepa, G. (2019, December). Evaluation of machine learning methodologies to predict stop delivery times from GPS data. Transportation Research Part C: Emerging Technologies, 109, 289–304. https://doi.org/10.1016/j.trc.2019.10.018
  • [18] Food Delivery Dataset. (2022, October 1). Kaggle. https://www.kaggle.com/datasets/gauravmalik26/food-delivery-dataset/code
  • [19] Murdoch, W. J., Singh, C. D., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences, 116(44), 22071-22080. https://doi.org/10.1073/pnas.1900654116
  • [20] Breiman, L. (2017). Classification and Regression Trees. Routledge. https://doi.org/10.1201/9781315139470
  • [21] Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  • [22] Yang, N., Korfiatis, N., Zissis, D., & Spanaki, K. (2023). Incorporating topic membership in review rating prediction from unstructured data: a gradient boosting approach. Annals of Operations Research. https://doi.org/10.1007/s10479-023-05336-z
  • [23] Larose, D. T., & Larose, C. D. (2014). Discovering knowledge in data: an introduction to data mining. John Wiley & Sons
  • [24] Pedregosa et al., (2012). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
  • [25] Lin, W., Liu, L., Zhao, G., & Jian, Z. (2023). Developing Hybrid DMO-XGBoost and DMO-RF Models for Estimating the Elastic Modulus of Rock. Mathematics, 11(18), Article 3886. https://doi.org/10.3390/math11183886
  • [26] Wang, X., Li, W., & Li, Q. (2021). A New Embedded Estimation Model for Soil Temperature Prediction. Scientific Programming, 2021, Article 5881018. https://doi.org/10.1155/2021/5881018
  • [27] Shahani, N. M., Kamran, M., Zheng, X., & Liu, C. (2022). Predictive modeling of drilling rate index using machine learning approaches: LSTM, simple RNN, and RFA. Petroleum Science and Technology, 40(5), 534–555. https://doi.org/10.1080/10916466.2021.2003386
  • [28] İnaç, H., Ayözen, Y. E., Atalan, A., & Dönmez, C. Ç. (2022). Estimation of Postal Service Delivery Time and Energy Cost with E-Scooter by Machine Learning Algorithms. Applied Sciences, 12(23), Article 12266. https://doi.org/10.3390/app122312266
  • [29] Ceylan, Z. (2021). The impact of COVID-19 on the electricity demand: a case study for Turkey. International Journal of Energy Research, 45(9), 13022–13039. https://doi.org/10.1002/er.6631
There are 29 citations in total.

Details

Primary Language English
Subjects Machine Learning (Other)
Journal Section Research Articles
Authors

Elmas Yalçinkaya 0009-0006-3570-2805

Ouranıa Areta Hızıroğlu 0000-0001-8607-6089

Publication Date May 1, 2024
Submission Date March 26, 2024
Acceptance Date April 29, 2024
Published in Issue Year 2024 Volume: 4 Issue: 1

Cite

APA Yalçinkaya, E., & Areta Hızıroğlu, O. (2024). A Comparative Analysis of Machine Learning Models for Time Prediction in Food Delivery Operations. Artificial Intelligence Theory and Applications, 4(1), 43-56.
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