Order Fulfillment pada Taksi Online dengan Mempertimbangkan Prioritas Penumpang Menggunakan Metode Recency, Frequency dan Monetary
Keywords:
Fesyen, Ilustrasi, Desain MotifAbstract
Along with the development of technology in Indonesia, online taxi companies are one of the fields that are starting to be developed. Just like other companies, online taxi companies are looking for profits, to achieve it, they need to maintain good relations with their passengers. That can be achieved by improving service to loyal passengers. In this study, factors will be applied to improve service to loyal passengers and drivers such as rating, number of trips, driver’s RFM score and passenger’s RFM score. The method used to segment drivers and passengers is RFM prioritization and Filtered RFM prioritization. The method used to pair the driver and passengers is the Hungarian method. This study shows that by adding additional factors such as driver and passenger RFM scores, driver ratings, and the number of trip drivers accompanied by a passenger pick-up time limit, don’t change the assign time, waiting time, and pickup time of passenger but can prioritize passengers and drivers according to those factors. In addition, internet speed also has a huge influence on website-based order fulfillment simulations.References
[1] Babicheva, T., Burghout, W., Andreasson, I., & Faul, N.
2018. The matching problem of empty vehicle redistribution
in autonomous taxi systems. Procedia Computer Science,
130, 119–125. DOI=10.1016/j.procs.2018.04.020.
[2] Birant, D. 2011. Data mining using RFM analysis. In Tech,
91–108. URI= https://www.intechopen.com/books/
knowledge-oriented-applications-in-data-mining/data-mining
-using-rfm-analysis.
[3] Christy, A. J., Umamakeswari, A., Priyatharsini, L., &
Neyaa, A. 2018. RFM Ranking – An Effective Approach to
Customer Segmentation. Journal of King Saud University -
Computer and Information Sciences.
DOI=10.1016/j.jksuci.2018.09.004.
[4] Diamant, A., & Baron, O. 2019. Double-sided matching
queues: Priority and impatient customers. Operations
Research Letters. DOI=10.1016/j.orl.2019.03.003.
[5] Eryn (2020). Perbandingan metode Tabu Search dan metode
Hungarian Algorithm untuk penentuan Driver Assignment
pada simulasi taksi online. URI=
https://dewey.petra.ac.id/catalog/digital/detail?id=46347.
[6] Kim, T. K. 2015. T test as a parametric statistic. Korean
Journal of Anesthesiology, 68(6), 540.
DOI=10.4097/kjae.2015.68.6.540.
[7] McCarty, J. A., & Hastak, M. 2007. Segmentation
approaches in data-mining: A comparison of RFM, CHAID,
and logistic regression. Journal of Business Research, 60(6),
656–662. DOI=10.1016/j.jbusres.2006.06.015.
[8] Munkres, J. 1957. Algorithms for the Assignment and
Transportation Problems. Journal of the Society for Industrial
and Applied Mathematics, 5(1), 32–38.
DOI=10.1137/0105003.
[9] Rabbani, Q., Khan, A., & Quddoos, A. 2019. Modified
Hungarian method for unbalanced assignment problems with
multiple jobs. Applied Mathematics and Computation, 361,
493–498. DOI=10.1016/j.amc.2019.05.041
[10] Zeithaml, V. A., Rust, R. T., & Lemon, K. N. 2001. The
Customer Pyramid: Creating and Serving Profitable
Customers. California Management Review, 43(4), 118–142.
DOI=10.2307/41166104.
[11] Zhang, L., Hu, T., Min, Y., Wu, G., Zhang, J., Feng, P., …
Ye, J. 2017. A Taxi Order Dispatch Model based On
Combinatorial Optimization. Proceedings of the 23rd ACM
SIGKDD International Conference on Knowledge Discovery
and Data Mining - KDD ’17. DOI=10.1145/3097983.