Abstract
Warehouse dwell time consists of working time and waiting time of trucks that have loading tasks in a warehouse. Warehouse dwell time prediction plays a crucial role for improving the truck scheduling strategies as well as the truck drivers’ experiences, and further proliferating the efficiency of warehouse logistics. Queuing theory based time prediction methods mainly focus on some queuing events with regularity to conform. However, the warehouse queuing system has a more complex structure. Specifically, the dwell time of any truck depends on the dwell time of its preceding trucks and the loading ability of warehouse. While warehouse loading ability keeps dynamically changing due to several factors like the capacity of the production line and loading device failures. This greatly increases the difficulty of warehouse dwell time predicting. In this paper, we first put forward the definition of block to represent the loading task statuses of different trucks. On the basis of that, we propose a deep learning based multi-block dwell time prediction model, called AMBD. It incorporates the loading ability of warehouse and the execution process of loading tasks of preceding trucks in the queue. Moreover, to proliferate the precision of warehouse dwell time prediction, we introduce attention mechanism to extract strong correlations among the trucks’ dwell time. Experimental results on real-world steel logistics data demonstrate the efficacy of our proposed models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Adhikari, B., Xu, X., Ramakrishnan, N., Prakash, B.A.: Epideep: exploiting embeddings for epidemic forecasting. In: Teredesai, A., Kumar, V., Li, Y., Rosales, R., Terzi, E., Karypis, G. (eds.) KDD, pp. 577–586. ACM (2019)
Aveklouris, A., Vlasiou, M., Zwart, B.: Bounds and limit theorems for a layered queueing model in electric vehicle charging. Queueing Syst. Theory Appl. 93(1–2), 83–137 (2019)
Breiman, L.: Better subset regression using the nonnegative garrote. Technometrics 37(4), 373–384 (1995)
Chaki, S., Doshi, P., Patnaik, P., Bhattacharya, S.: Attentive RNNs for continuous-time emotion prediction in music clips. In: Chhaya, N., Jaidka, K., Healey, J., Ungar, L., Sinha, A.R. (eds.) AAAI, CEUR Workshop Proceedings, vol. 2614, pp. 36–46. CEUR-WS.org (2020)
Chen, Y., Whitt, W.: Algorithms for the upper bound mean waiting time in the GI/GI/1 queue. Queueing Syst. Theory Appl. 94(3–4), 327–356 (2020)
Cheng, H., et al.: Wide & deep learning for recommender systems. In: Karatzoglou, A., et al. (eds.) DLRS@RecSys, pp. 7–10. ACM (2016)
Dasgupta, S., Osogami, T.: Nonlinear dynamic boltzmann machines for time-series prediction. In: Singh, S.P., Markovitch, S. (eds.) AAAI, pp. 1833–1839. AAAI Press (2017)
Feinberg, E.A., Yang, F.: Optimal pricing for a gi/m/k/n queue with several customer types and holding costs. Queueing Syst. Theory Appl. 82(1–2), 103–120 (2016)
Fu, K., Meng, F., Ye, J., Wang, Z.: Compacteta: a fast inference system for travel time prediction. In: Gupta, R., Liu, Y., Tang, J., Prakash, B.A. (eds.) KDD, pp. 3337–3345. ACM (2020)
Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: Deepfm: a factorization-machine based neural network for CTR prediction. In: Sierra, C. (ed.) IJCAI, pp. 1725–1731. ijcai.org (2017)
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)
Horváth, G.: Waiting time and queue length analysis of markov-modulated fluid priority queues. Queueing Syst. Theory Appl. 95(1), 69–95 (2020)
Legros, B.: M/G/1 queue with event-dependent arrival rates. Queueing Syst. Theory Appl. 89(3–4), 269–301 (2018)
Li, J.: Structured prediction in time series data. In: Singh, S.P., Markovitch, S. (eds.) AAAI, pp. 5042–5043. AAAI Press (2017)
Liu, C., Hoi, S.C.H., Zhao, P., Sun, J.: Online ARIMA algorithms for time series prediction. In: Schuurmans, D., Wellman, M.P. (eds.) AAAI, pp. 1867–1873. AAAI Press (2016)
Shajin, D., Krishnamoorthy, A., Dudin, A.N., Joshua, V.C., Jacob, V.: On a queueing-inventory system with advanced reservation and cancellation for the next K time frames ahead: the case of overbooking. Queueing Syst. Theory Appl. 94(1–2), 3–37 (2020)
Stanford, D.A., Taylor, P., Ziedins, I.: Waiting time distributions in the accumulating priority queue. Queueing Syst. 77(3), 297–330 (2013). https://doi.org/10.1007/s11134-013-9382-6
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. Ser. B (Methodol.) 58(1), 267–288 (1996)
Vassøy, B., Ruocco, M., de Souza da Silva, E., Aune, E.: Time is of the essence: a joint hierarchical RNN and point process model for time and item predictions. In: Culpepper, J.S., Moffat, A., Bennett, P.N., Lerman, K. (eds.) WSDM, pp. 591–599. ACM (2019)
Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems, pp. 5998–6008 (2017)
Wang, Z., Fu, K., Ye, J.: Learning to estimate the travel time. In: Guo, Y., Farooq, F. (eds.) KDD, pp. 858–866. ACM (2018)
Zhan, D., Weiss, G.: Many-server scaling of the n-system under FCFS-ALIS. Queueing Syst. Theory Appl. 88(1–2), 27–71 (2018)
Zheng, C., Fan, X., Wang, C., Qi, J.: GMAN: a graph multi-attention network for traffic prediction. In: AAAI, pp. 1234–1241. AAAI Press (2020)
Zhou, X., Shen, Y., Huang, L., Zang, T., Zhu, Y.: Multi-level attention networks for multi-step citywide passenger demands prediction. IEEE Trans. Knowl. Data Eng. 33(5), 2096–2108 (2021)
Zhu, L., et al.: Order fulfillment cycle time estimation for on-demand food delivery. In: Gupta, R., Liu, Y., Tang, J., Prakash, B.A. (eds.) KDD, pp. 2571–2580. ACM (2020)
Acknowledgments
This research was supported by NSFC (Nos. 62072180, U1911203 and U1811264).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Lv, X., Zhao, W., Mao, J., Guo, Y., Zhou, A. (2021). AMBD: Attention Based Multi-Block Deep Learning Model for Warehouse Dwell Time Prediction. In: Zhang, W., Zou, L., Maamar, Z., Chen, L. (eds) Web Information Systems Engineering – WISE 2021. WISE 2021. Lecture Notes in Computer Science(), vol 13081. Springer, Cham. https://doi.org/10.1007/978-3-030-91560-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-91560-5_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-91559-9
Online ISBN: 978-3-030-91560-5
eBook Packages: Computer ScienceComputer Science (R0)