Load prediction of parcel pick-up points: model-driven vs data-driven approaches

References

• Andersen, P. K., and N. Keiding. 2002. “Multi-state Models for Event History Analysis.” Statistical Methods in Medical Research 11 (2): 91–115. https://doi.org/10.1191/0962280202SM276ra.
   PubMed Web of Science ®Google Scholar
• Archetti, C., and L. Bertazzi. 2021. “Recent Challenges in Routing and Inventory Routing: E-commerce and Last-mile Delivery.” Networks 77 (2): 255–268. https://doi.org/10.1002/net.v77.2.
   Web of Science ®Google Scholar
• Box, G. E. P., G. M. Jenkins, G. C. Reinsel, and G. M. Ljung. 2016. Time Series Analysis: Forecasting and Control. 5th ed. Wiley Series in Probability and Statistics. Hoboken, New Jersey: John Wiley & Sons, Inc.
   Google Scholar
• Brajon, D., C. Ropital, C. Delaporte, C. Tarquis, and F. Awada. 2016. “Comment Ameliorer la Performance Logistique du E-commerce ?” Technical Report. Institut Paris Region.
   Google Scholar
• Breiman, L.. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Burger, C. J. S. C., M. Dohnal, M. Kathrada, and R. Law. 2001. “A Practitioners Guide to Time-series Methods for Tourism Demand Forecasting – A Case Study of Durban, South Africa.” Tourism Management 22 (4): 403–409. https://doi.org/10.1016/S0261-5177(00)00068-6.
   Web of Science ®Google Scholar
• Caicedo-Torres, W., and F. Payares. 2016. “A Machine Learning Model for Occupancy Rates and Demand Forecasting in the Hospitality Industry.” In Advances in Artificial Intelligence – IBERAMIA 2016, edited by M. M. y Gomez, H. J. Escalante, A. Segura, and J. de Dios Murillo, Cham, 201–211. Springer International Publishing.
   Google Scholar
• Cavazos-Cadena, R.. 1994. “Computing the Asymptotic Covariance Matrix of a Vector of Sample Autocorrelations for ARMA Processes.” Applied Mathematics and Computation 64 (2–3): 121–137. https://doi.org/10.1016/0096-3003(94)90058-2.
   Web of Science ®Google Scholar
• Cayirli, T., and E. Veral. 2009. “Outpatient Scheduling in Health Care: A Review of Literature.” Production and Operations Management 12 (4): 519–549. https://doi.org/10.1111/poms.2003.12.issue-4.
   Google Scholar
• Chen, C. W. S., and S. Lee. 2016. “Generalized Poisson Autoregressive Models for Time Series of Counts.” Computational Statistics & Data Analysis99:51–67. https://doi.org/10.1016/j.csda.2016.01.009.
   Web of Science ®Google Scholar
• Hall, D. B. 2000. “Zero-Inflated Poisson and Binomial Regression With Random Effects: A Case Study.” Biometrics 56 (4): 1030–1039. https://doi.org/10.1111/j.0006-341X.2000.01030.x.
   PubMed Web of Science ®Google Scholar
• Hamilton, J. D. 1989. “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica 57 (2): 357. https://doi.org/10.2307/1912559.
   Web of Science ®Google Scholar
• Hochreiter, S., and J. Schmidhuber. 1997. “Long Short-Term Memory.” Neural Computation 9 (8): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
   PubMed Web of Science ®Google Scholar
• Holt, C. C. 2004. “Forecasting Seasonals and Trends by Exponentially Weighted Moving Averages.” International Journal of Forecasting 20 (1): 5–10. https://doi.org/10.1016/j.ijforecast.2003.09.015.
   Web of Science ®Google Scholar
• IPC. 2015. IPC Strategic Perspectives on the Postal Market. Technical Report. International Post Corporation.
   Google Scholar
• Iwan, S., K. Kijewska, and J. Lemke. 2016. “Analysis of Parcel Lockers' Efficiency As the Last Mile Delivery Solution – The Results of the Research in Poland.” Transportation Research Procedia 12:644–655. https://doi.org/10.1016/j.trpro.2016.02.018.
   Google Scholar
• Kedia, A., D. Kusumastuti, and A. Nicholson. 2017. “Acceptability of Collection and Delivery Points From Consumers' Perspective: A Qualitative Case Study of Christchurch City.” Case Studies on Transport Policy 5 (4): 587–595. https://doi.org/10.1016/j.cstp.2017.10.009.
   Web of Science ®Google Scholar
• Kelly, F. P.. 1991. “Loss Networks.” The Annals of Applied Probability 1 (3): 319–378. Publisher: Institute of Mathematical Statistics. https://doi.org/10.1214/aoap/1177005872.
   Google Scholar
• Kleinrock, L. 1975. “Birth-Death Queueing Systems in Equilibrium.” In Queueing Systems, Vol. 1, 105–115. New York: Wiley.
   Google Scholar
• Kobayashi, H., and B. L. Mark. 2002. “Generalized Loss Models and Queueing-loss Networks.” International Transactions in Operational Research 9 (1): 97–112. https://doi.org/10.1111/itor.2002.9.issue-1.
   Google Scholar
• Louppe, G., L. Wehenkel, A. Sutera, and P. Geurts. 2013. “Understanding Variable Importances in Forests of Randomized Trees.” In Advances in Neural Information Processing Systems, edited by C. J. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, Vol. 26. Curran Associates, Inc.
   Google Scholar
• Ma, S., R. Fildes, and T. Huang. 2016. “Demand Forecasting with High Dimensional Data: The Case of SKU Retail Sales Forecasting with Intra- and Inter-category Promotional Information.” European Journal of Operational Research 249 (1): 245–257. https://doi.org/10.1016/j.ejor.2015.08.029.
   Web of Science ®Google Scholar
• M., Spyros, E. Spiliotis, and V. Assimakopoulos. 2018. “Statistical and Machine Learning Forecasting Methods: Concerns and Ways Forward.” PloS One 13 (3): e0194889. https://doi.org/10.1371/journal.pone.0194889.
   PubMed Web of Science ®Google Scholar
• Manolakis, D., N. Bosowski, and V. K. Ingle. 2019. “Count Time-Series Analysis: A Signal Processing Perspective.” IEEE Signal Processing Magazine 36 (3): 64–81. https://doi.org/10.1109/MSP.79.
   Web of Science ®Google Scholar
• Monaco, J. V., and C. C. Tappert. 2018. “The Partially Observable Hidden Markov Model and Its Application to Keystroke Dynamics.” Pattern Recognition 76:449–462. https://doi.org/10.1016/j.patcog.2017.11.021.
   Web of Science ®Google Scholar
• Morganti, E., L. Dablanc, and F. Fortin. 2014. “Final Deliveries for Online Shopping: The Deployment of Pickup Point Networks in Urban and Suburban Areas.” Research in Transportation Business & Management 11:23–31. https://doi.org/10.1016/j.rtbm.2014.03.002.
   Google Scholar
• Morganti, E., S. Seidel, C. Blanquart, L. Dablanc, and B. Lenz. 2014. “The Impact of E-commerce on Final Deliveries: Alternative Parcel Delivery Services in France and Germany.” Transportation Research Procedia 4:178–190. https://doi.org/10.1016/j.trpro.2014.11.014.
   Google Scholar
• Mucowska, M.. 2021. “Trends of Environmentally Sustainable Solutions of Urban Last-Mile Deliveries on the E-Commerce Market – A Literature Review.” Sustainability 13 (11): 5894. https://doi.org/10.3390/su13115894.
   Web of Science ®Google Scholar
• Petalas, Y. G., A. Ammari, P. Georgakis, and C. Nwagboso. 2017. “A Big Data Architecture for Traffic Forecasting Using Multi-Source Information.” In Algorithmic Aspects of Cloud Computing, edited by T. Sellis and K. Oikonomou, Cham, 65–83. Springer International Publishing.
   Google Scholar
• Pliszczuk, D., P. Lesiak, K. Zuk, and T. Cieplak. 2021. “Forecasting Sales in the Supply Chain Based on the LSTM Network: The Case of Furniture Industry.” European Research Studies Journal XXIV (Special Issue 2): 627–636. https://doi.org/10.35808/ersj/2291.
   Google Scholar
• Punia, S., K. Nikolopoulos, S. Prakash Singh, J. K. Madaan, and K. Litsiou. 2020. “Deep Learning with Long Short-term Memory Networks and Random Forests for Demand Forecasting in Multi-channel Retail.” International Journal of Production Research 58 (16): 4964–4979. https://doi.org/10.1080/00207543.2020.1735666.
   Web of Science ®Google Scholar
• RSF. 2020. B2C E-commerce Market Size, Share & Trends Analysis Report by Type (B2C Retailers, Classifieds), by Application (Home Decor & Electronics, Clothing & Footwear), by Region and Segment Forecasts, 2020 – 2027. Technical Report. ResearchAndMarkets.com.
   Google Scholar
• Sak, H., A. Senior, and F. Beaufays. 2014. “Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition.” Publisher: arXiv Version Number: 1. https://arxiv.org/abs/1402.1128.
   Google Scholar
• Salari, N., S. Liu, and Z.-J. M. Shen.. 2020. “Real-Time Delivery Time Forecasting and Promising in Online Retailing: When Will Your Package Arrive?.” SSRN Electronic Journal 24 (3): 1421–1436.
   Google Scholar
• Stewart, W. J. 2009. “Elementary Queuing Theory.” In Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling, 385–440. Princeton, (NJ): Princeton University Press.
   Google Scholar
• Sudarshan, V. K., M. Brabrand, T. M. Range, and U. K. Wiil. 2021. “Performance Evaluation of Emergency Department Patient Arrivals Forecasting Models by Including Meteorological and Calendar Information: A Comparative Study.” Computers in Biology and Medicine 135:104541. https://doi.org/10.1016/j.compbiomed.2021.104541.
   PubMed Web of Science ®Google Scholar
• Theodoridis, S.. 2015. Machine Learning: A Bayesian and Optimization Perspective. London: Elsevier Academic Press.
   Google Scholar
• Vairagade, N., D. Logofatu, F. Leon, and F. Muharemi. 2019. “Demand Forecasting Using Random Forest and Artificial Neural Network for Supply Chain Management.” In Computational Collective Intelligence, edited by N. T. Nguyen, R. Chbeir, E. Exposito, P. Aniorte, and B. Trawinski, Cham, 328–339. Springer International Publishing.
   Google Scholar
• Van Gils, T., K. Ramaekers, A. Caris, and M. Cools. 2017. “The Use of Time Series Forecasting in Zone Order Picking Systems to Predict Order Pickers' Workload.” International Journal of Production Research 55 (21): 6380–6393. https://doi.org/10.1080/00207543.2016.1216659.
   Web of Science ®Google Scholar
• Wang, H., X. Lin, and L. Qian. 2009. “Crytic Period Analysis Model of Hydrological Process and Its Application.” Hydrological Processes 23 (13): 1834–1843. https://doi.org/10.1002/hyp.v23:13.
   Web of Science ®Google Scholar
• Weingarten, J., and S. Spinler. 2020. “Shortening Delivery Times by Predicting Customers' Online Purchases: A Case Study in the Fashion Industry.” Information Systems Management 38 (4): 287–308. https://doi.org/10.1080/10580530.2020.1814459.
   Web of Science ®Google Scholar
• Weltevreden, J. W. J. 2008. “B2C E-commerce Logistics: the Rise of Collection-and-delivery Points in The Netherlands.” International Journal of Retail & Distribution Management 36 (8): 638–660. https://doi.org/10.1108/09590550810883487.
   Google Scholar
• Whitt, W., and X. Zhang. 2019. “Forecasting Arrivals and Occupancy Levels in An Emergency Department.” Operations Research for Health Care 21:1–18. https://doi.org/10.1016/j.orhc.2019.01.002.
   Web of Science ®Google Scholar
• Wiler, J. L., R. T. Griffey, and T. Olsen. 2011. “Review of Modeling Approaches for Emergency Department Patient Flow and Crowding Research: Modeling Approaches for ED Patient Flow and Crowding Research.” Academic Emergency Medicine 18 (12): 1371–1379. https://doi.org/10.1111/acem.2011.18.issue-12.
   PubMed Web of Science ®Google Scholar
• Xu, X., Y. Shen, W. A. Chen, Y. Gong, and H. Wang. 2021. “Data-driven Decision and Analytics of Collection and Delivery Point Location Problems for Online Retailers.” Omega 100:102280. https://doi.org/10.1016/j.omega.2020.102280.
   Web of Science ®Google Scholar