IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v21y2021i2d10.1007_s12351-019-00477-1.html
   My bibliography  Save this article

Container flow forecasting through neural networks based on metaheuristics

Author

Listed:
  • M. Milenković

    (Zaragoza Logistics Center
    University of Belgrade)

  • N. Milosavljevic

    (State University of Novi Pazar)

  • N. Bojović

    (University of Belgrade)

  • S. Val

    (Zaragoza Logistics Center)

Abstract

In this paper we propose a fuzzy neural network prediction approach based on metaheuristics for container flow forecasting. The approach uses fuzzy if–then rules for selection between two different heuristics for developing neural network architecture, simulated annealing and genetic algorithm, respectively. These non-parametric models are compared with traditional parametric ARIMA technique. Time series composed from monthly container traffic observations for Port of Barcelona are used for model developing and testing. Models are compared based on the most important criteria for performance evaluation and for each of the data sets (total container traffic, loaded, unloaded, transit and empty) the appropriate model is selected.

Suggested Citation

  • M. Milenković & N. Milosavljevic & N. Bojović & S. Val, 2021. "Container flow forecasting through neural networks based on metaheuristics," Operational Research, Springer, vol. 21(2), pages 965-997, June.
    • Handle: RePEc:spr:operea:v:21:y:2021:i:2:d:10.1007_s12351-019-00477-1
    DOI: 10.1007/s12351-019-00477-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-019-00477-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12351-019-00477-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sang-Yoon Lee & Hyunwoo Lim & Hwa-Joong Kim, 2017. "Forecasting container port volume: implications for dredging," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(2), pages 296-314, June.
    2. Osborn, Denise R, et al, 1988. "Seasonality and the Order of Integration for Consumption," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 50(4), pages 361-377, November.
    3. Arifovic, Jasmina & Gençay, Ramazan, 2001. "Using genetic algorithms to select architecture of a feedforward artificial neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 289(3), pages 574-594.
    4. Peter Schulze & Alexander Prinz, 2009. "Forecasting container transshipment in Germany," Applied Economics, Taylor & Francis Journals, vol. 41(22), pages 2809-2815.
    5. Twrdy, Elen & Batista, Milan, 2016. "Modeling of container throughput in Northern Adriatic ports over the period 1990–2013," Journal of Transport Geography, Elsevier, vol. 52(C), pages 131-142.
    6. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    7. Sexton, Randall S. & Dorsey, Robert E. & Johnson, John D., 1999. "Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing," European Journal of Operational Research, Elsevier, vol. 114(3), pages 589-601, May.
    8. Ruiz-Aguilar, J.J. & Turias, I.J. & Jiménez-Come, M.J., 2014. "Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 67(C), pages 1-13.
    9. William Lam & Y. Tang & K. Chan & Mei-Lam Tam, 2006. "Short-term Hourly Traffic Forecasts using Hong Kong Annual Traffic Census," Transportation, Springer, vol. 33(3), pages 291-310, May.
    10. Gu Pang & Bartosz Gebka, 2017. "Forecasting container throughput using aggregate or terminal-specific data? The case of Tanjung Priok Port, Indonesia," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2454-2469, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jin, Jiahuan & Ma, Mingyu & Jin, Huan & Cui, Tianxiang & Bai, Ruibin, 2023. "Container terminal daily gate in and gate out forecasting using machine learning methods," Transport Policy, Elsevier, vol. 132(C), pages 163-174.
    2. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    3. Shaojun Lu & Min Kong & Zhiping Zhou & Xinbao Liu & Siwen Liu, 2022. "A hybrid metaheuristic for a semiconductor production scheduling problem with deterioration effect and resource constraints," Operational Research, Springer, vol. 22(5), pages 5405-5440, November.
    4. Truong Ngoc Cuong & Sam-Sang You & Le Ngoc Bao Long & Hwan-Seong Kim, 2022. "Seaport Resilience Analysis and Throughput Forecast Using a Deep Learning Approach: A Case Study of Busan Port," Sustainability, MDPI, vol. 14(21), pages 1-25, October.
    5. Anqiang Huang & Xinjun Liu & Changrui Rao & Yi Zhang & Yifan He, 2022. "A New Container Throughput Forecasting Paradigm under COVID-19," Sustainability, MDPI, vol. 14(5), pages 1-20, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gu Pang & Bartosz Gebka, 2017. "Forecasting container throughput using aggregate or terminal-specific data? The case of Tanjung Priok Port, Indonesia," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2454-2469, May.
    2. Anqiang Huang & Xinjun Liu & Changrui Rao & Yi Zhang & Yifan He, 2022. "A New Container Throughput Forecasting Paradigm under COVID-19," Sustainability, MDPI, vol. 14(5), pages 1-20, March.
    3. Jin, Jiahuan & Ma, Mingyu & Jin, Huan & Cui, Tianxiang & Bai, Ruibin, 2023. "Container terminal daily gate in and gate out forecasting using machine learning methods," Transport Policy, Elsevier, vol. 132(C), pages 163-174.
    4. Geraint Johnes, 2000. "Up Around the Bend: Linear and nonlinear models of the UK economy compared," International Review of Applied Economics, Taylor & Francis Journals, vol. 14(4), pages 485-493.
    5. Joo, Rocío & Bertrand, Sophie & Chaigneau, Alexis & Ñiquen, Miguel, 2011. "Optimization of an artificial neural network for identifying fishing set positions from VMS data: An example from the Peruvian anchovy purse seine fishery," Ecological Modelling, Elsevier, vol. 222(4), pages 1048-1059.
    6. Yi Xiao & Shouyang Wang & Ming Xiao & Jin Xiao & Yi Hu, 2017. "The Analysis for the Cargo Volume with Hybrid Discrete Wavelet Modeling," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(03), pages 851-863, May.
    7. Feng, Hongxiang & Grifoll, Manel & Zheng, Pengjun, 2019. "From a feeder port to a hub port: The evolution pathways, dynamics and perspectives of Ningbo-Zhoushan port (China)," Transport Policy, Elsevier, vol. 76(C), pages 21-35.
    8. Ruiz-Aguilar, J.J. & Turias, I.J. & Jiménez-Come, M.J., 2014. "Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 67(C), pages 1-13.
    9. Truong Ngoc Cuong & Le Ngoc Bao Long & Hwan-Seong Kim & Sam-Sang You, 2023. "Data analytics and throughput forecasting in port management systems against disruptions: a case study of Busan Port," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 61-89, March.
    10. Apostolos Ampountolas, 2021. "Modeling and Forecasting Daily Hotel Demand: A Comparison Based on SARIMAX, Neural Networks, and GARCH Models," Forecasting, MDPI, vol. 3(3), pages 1-16, August.
    11. Long Wen & Chang Liu & Haiyan Song, 2019. "Forecasting tourism demand using search query data: A hybrid modelling approach," Tourism Economics, , vol. 25(3), pages 309-329, May.
    12. Francesco Parola & Giovanni Satta & Theo Notteboom & Luca Persico, 2021. "Revisiting traffic forecasting by port authorities in the context of port planning and development," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(3), pages 444-494, September.
    13. Harish Kumar Ghritlahre & Purvi Chandrakar & Ashfaque Ahmad, 2021. "A Comprehensive Review on Performance Prediction of Solar Air Heaters Using Artificial Neural Network," Annals of Data Science, Springer, vol. 8(3), pages 405-449, September.
    14. Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2005. "Testing for Long Memory and Nonlinear Time Series: A Demand for Money Study," Trinity Economics Papers tep20021, Trinity College Dublin, Department of Economics.
    15. Bardsen, G. & Klovland, J.T., 1990. "Finding The Rigth Nominal Anchor: The Cointegration Of Money, Credit And Nominal Income In Norway," The Warwick Economics Research Paper Series (TWERPS) 350, University of Warwick, Department of Economics.
    16. Ghiassi, M. & Saidane, H. & Zimbra, D.K., 2005. "A dynamic artificial neural network model for forecasting time series events," International Journal of Forecasting, Elsevier, vol. 21(2), pages 341-362.
    17. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    18. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    19. Andrew Burke, 1996. "The dynamics of product differentiation in the British record industry," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 20(2), pages 145-164, June.
    20. Nataša Glišović & Miloš Milenković & Nebojša Bojović & Libor Švadlenka & Zoran Avramović, 2016. "A hybrid model for forecasting the volume of passenger flows on Serbian railways," Operational Research, Springer, vol. 16(2), pages 271-285, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:operea:v:21:y:2021:i:2:d:10.1007_s12351-019-00477-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.