Predictive Delivery Man Assignment Problem using Deep Learning

Authors

Rahmadini Payla Juarsa, Agro-industrial Engineering Study Program, Institut Pertanian Bogor, Bogor 16680, IndonesiaFollow
Taufik Djatna, Agro-industrial Engineering Study Program, Institut Pertanian Bogor, Bogor 16680, Indonesia

Abstract

Dispatching is a critical part in current online shopping. It relates to how the delivery man assignment should minimize cost along with the service from a source to an end customer with an appropriate scheduled time. The problem arises as neither enough products to deliver nor delivery men are available for dispatch, resulting in suboptimal service and a waste of money. The study aimed to formulate the cost of restaurant dispatching for inducing a deep learning-based solution with the gated recurrent unit recurrent neural network to receive hourly order data and to engage the result for near feature delivery man schedule with minimum cost. The result showed that cost formulation minimized the number of delivery men times the wage per hour with the constraints of each delivery man carrying a maximum of five orders in one way and 11 work hours/day. The deep learning input model used 1078 historical data which were filtered using the Savitzky-Golay method. The root mean square errors of training and testing were 2.35 and 2.41, respectively. Moreover, the number of delivery men every hour was found in a range from one to four people. Furthermore, the deep learning approach saved costs of up to 43.8%.

Bahasa Abstract

Prediksi Masalah Penugasan Kurir dengan Pembelajaran Mendalam. Pengiriman merupakan salah satu hal yang penting dalam model belanja online. Hal itu berhubungan dengan bagaimana penugasan kurir untuk mengantar pesanan hingga ke konsumen dengan cepat dan biaya yang ditimbulkan sesedikit mungkin. Permasalahan yang terjadi adalah jika ada ketidakseimbangan antara jumlah kurir dan jumlah pesanan yang harus diantarkan sehingga menyebabkan pelayanan tidak optimal dan berakhir pemborosan. Tujuan dari penelitian ini adalah memformulasi model biaya pengiriman yang terdapat di restoran, menyusun solusi deep learning gated recurrent Unit (GRU) recurrent neural network (RNN) untuk mendapatkan data pesanan setiap jam, dan menggunakan hasil yang didapat untuk menyusun jadwal penugasan kurir yang menghasilkan biaya minimum. Hasil yang didapat adalah formulasi dari biaya penugasan adalah meminimumkan jumlah kurir dikali dengan upah tiap jamnya, dengan batasan masing-masing kurir maksimum membawa 5 pesanan dalam sekali jalan dan 11 jam kerja/hari. Input dalam model deep learning adalah 1078 data historis pesanan online yang sudah disaring menggunakan metode Savitzky-Golay. RMSE dari data pelatihan dan data percobaan masing-masing 2.35 dan 2.41. Jumlah kurir yang didapat dari metode ini adalah 1-4 orang dari yang sebelumnya 3-4 orang. Metode ini mampu menghemat biaya hingga 43.8%.

References

R. Aryanto, A. Fontana, A.Z. Afiff, Strat. Hum. Res. Man. Procedia Soc. Behav. Sci. 211 (2015) 874.

A. Pizam, V. Shapoval, T. Ellis, Int. J. Contemporary Hosp. Man. 28 (2016) 2.

R. Burkard, M. Dell’Amico, S. Martello, Introduction in Assignment Problem, Siam, Philadelphia, 2009, p.1.

G. Werner, S. Yang, K. McConky, Proceedings of the 12th Annual Conference on Cyber and Information Security Research, Oak Ridge, 2017, p.4.

A.A. Oluyinka, A.C. Korede, J. Appl. Math. (2014) 1.

C.W.M. Noor, R. Mamat, G. Najafi, W.B.W. Nik, M. Fadhil, IOP Conf. Ser. Mater. Sci. Eng. 100 (2015) 1.

T. Kim, T. Yoon, Int. J. Mach. Learn. Compu. 5/6 (2015) 471.

L. Mou, P. Ghamisi, X. Zhu, IEEE Tran. Geosci. Rem. Sensing. 5 (2017) 3639.

J. Liu, C. Wu, J. Wang, Inf. Sci. 423 (2018) 50.

B. Zhang, J. Peng, Appl. Math. Model. 37 (2013) 6458.

A.N. Gani, V.N. Mohamed. Intern. J. Fuzzy Math. Arch. 2 (2013) 8.

A.M. Ibrahim, N.H. El-Amary. J. Elect. Syst. Info. Technol. 5 (2018) 216.

M. Rhode, P. Burnap, K. Jones. Comp. Sc. 77 (2018) 578.

V. Athira, P. Geetha, R. Vinayakumar, K.P. Soman. Proc. Comp. Sci. 132 (2018) 1394.

R. Vinayakumar, K.P. Soman, P. Poornachandran. International Conferences on Advances in Computing, Communications and Informatics, Karnataka, 2017,.p.2353.

Y. Kaneko, K. Yada. International Conference on Data Mining Workshops, Barcelona, 2016, p.531.

K. Cho, Bv. Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, Y. Bengio, Learning phrase representations using RNN encoder-decoder for statistical machine translation, arXiv:1406.1078v3 [cs.CL], 2014.

S. Hochreiter, J. Schmidhuber. Neur. Comput. 9 (1997) 1735.

R. Fu, Z. Zhang, L. Li. 31st Youth Academic Annual Conference of Chinese Association of Automation, Wuhan, 2016, p.324.

A. Savitzky, M.J. Golay. Anal. Chem. 36 (1964) 1627.

J. Li, H. Deng, P. Li, B. Yu. Appl. Phys. B. 120 (2015) 207.

H. Wang, J. Peng, C. Xie, Y. Bao, Y. He. Sens. 15/5 (2015) 11889.

Y. Liu, B. Dang, Y. Li, H. Lin, H. Ma. 2016. Acta Geophys. 64 (2016) 101.

T. Salimans, D.P. Kingma. 30th Conference on Neural Information Processing Systems, Barcelona, 2016, p.1.

Y. LeChun, Y. Bengio, G. Hinton. Nat. 521 (2015) 436.

N. Kamairuddin, S.S.A. Gani, H.R.F. Masoumi, M. Basri, P. Hashim, N.M. Mokhtar, M.E. Lane. RSC Adv. 5 (2015) 68632.

J. Zbontar, Y. LeChun, Computing the stereo matching with a convolutional neural network. Computer Vision Foundation, arXiv:1409.4326v2 [cs.CV], 2015.

A. Krizhevsky, I. Sutskever, G.E. Hinton. 2012. Proceeding Advance Neural Information Processing Systems, Nevada, 2012, p.1097.

Y. Gu, B.K. Wyle, S.P. Boyte, J. Picotte, D.M. Howard, K. Smith, K.J. Nelson, Remote Sensing. 8 (2016) 1.

Recommended Citation

Juarsa, Rahmadini Payla and Djatna, Taufik (2021) "Predictive Delivery Man Assignment Problem using Deep Learning," Makara Journal of Technology: Vol. 25: Iss. 2, Article 7.
DOI: 10.7454/mst.v25i2.3700
Available at: https://scholarhub.ui.ac.id/mjt/vol25/iss2/7