IDEAS home Printed from https://ideas.repec.org/a/spr/infotm/v18y2017i3d10.1007_s10799-016-0267-3.html
   My bibliography  Save this article

A decision support system for improved resource planning and truck routing at logistic nodes

Author

Listed:
  • Alessandro Hill

    (Hamburg University of Technology)

  • Jürgen W. Böse

    (Hamburg University of Technology)

Abstract

In this paper, we present an innovative decision support system that simultaneously provides predictive analytics to logistic nodes as well as to collaborating truck companies. Logistic nodes, such as container terminals, container depots or container loading facilities, face heavy workloads through a large number of truck arrivals during peak times. At the same time, truck companies suffer from augmented waiting times. The proposed system provides forecasted truck arrival rates to the nodes and predicted truck gate waiting times at the nodes to the truck companies based on historical data, economic and environmental impact factors. Based on the expected workloads, the node personnel and machinery can be planned more efficiently. Truck companies can adjust their route planning in order to minimize waiting times. Consequently, both sides benefit from reduced truck waiting times while reducing traffic congestion and air pollution. We suggest a flexible cloud based service that incorporates an advanced forecasting engine based on artificial intelligence capable of providing individual predictions for users on all planning levels. In a case study we report forecasting results obtained for the truck waiting times at an empty container depot using artificial neural networks.

Suggested Citation

  • Alessandro Hill & Jürgen W. Böse, 2017. "A decision support system for improved resource planning and truck routing at logistic nodes," Information Technology and Management, Springer, vol. 18(3), pages 241-251, September.
  • Handle: RePEc:spr:infotm:v:18:y:2017:i:3:d:10.1007_s10799-016-0267-3
    DOI: 10.1007/s10799-016-0267-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10799-016-0267-3
    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/s10799-016-0267-3?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. 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.
    2. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 10, pages 515-554, Elsevier.
    3. Itf, 2015. "The Impact of Mega-Ships," International Transport Forum Policy Papers 10, OECD Publishing.
    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. Dornemann, Jorin & Rückert, Nicolas & Fischer, Kathrin & Taraz, Anusch, 2020. "Artificial intelligence and operations research in maritime logistics," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Data Science in Maritime and City Logistics: Data-driven Solutions for Logistics and Sustainability. Proceedings of the Hamburg International Conferen, volume 30, pages 337-381, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    2. Valentin Carlan & Dries Naudts & Pieter Audenaert & Bart Lannoo & Thierry Vanelslander, 2019. "Toward implementing a fully automated truck guidance system at a seaport: identifying the roles, costs and benefits of logistics stakeholders," Journal of Shipping and Trade, Springer, vol. 4(1), pages 1-24, December.
    3. Loske, Dominic & Klumpp, Matthias, 2021. "Human-AI collaboration in route planning: An empirical efficiency-based analysis in retail logistics," International Journal of Production Economics, Elsevier, vol. 241(C).

    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. Alessandro Hill & Jürgen W. Böse, 0. "A decision support system for improved resource planning and truck routing at logistic nodes," Information Technology and Management, Springer, vol. 0, pages 1-11.
    2. Safari, Ali & Davallou, Maryam, 2018. "Oil price forecasting using a hybrid model," Energy, Elsevier, vol. 148(C), pages 49-58.
    3. Mirakyan, Atom & Meyer-Renschhausen, Martin & Koch, Andreas, 2017. "Composite forecasting approach, application for next-day electricity price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 228-237.
    4. Jonathan Fabián Dato & Matías Gabriel Dinápoli & Enrique Eduardo D’Onofrio & Claudia Gloria Simionato, 2024. "On water level forecasting using artificial neural networks: the case of the Río de la Plata Estuary, Argentina," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(11), pages 9753-9776, September.
    5. Golnoosh Babaei & Shahrooz Bamdad, 2021. "A New Hybrid Instance-Based Learning Model for Decision-Making in the P2P Lending Market," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 419-432, January.
    6. Christoph Gleue & Dennis Eilers & Hans-Jörg Mettenheim & Michael H. Breitner, 2019. "Decision Support for the Automotive Industry," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(4), pages 385-397, August.
    7. Exterkate, Peter & Groenen, Patrick J.F. & Heij, Christiaan & van Dijk, Dick, 2016. "Nonlinear forecasting with many predictors using kernel ridge regression," International Journal of Forecasting, Elsevier, vol. 32(3), pages 736-753.
    8. Poncela, Pilar & Ruiz Ortega, Esther, 2012. "More is not always better : back to the Kalman filter in dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS ws122317, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Balkin, Sandy, 2001. "On Forecasting Exchange Rates Using Neural Networks: P.H. Franses and P.V. Homelen, 1998, Applied Financial Economics, 8, 589-596," International Journal of Forecasting, Elsevier, vol. 17(1), pages 139-140.
    10. Chen, Qitong & Hong, Yongmiao & Li, Haiqi, 2024. "Time-varying forecast combination for factor-augmented regressions with smooth structural changes," Journal of Econometrics, Elsevier, vol. 240(1).
    11. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    12. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    13. Davide Pettenuzzo & Francesco Ravazzolo, 2016. "Optimal Portfolio Choice Under Decision‐Based Model Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1312-1332, November.
    14. Francesco Lisi & Ismail Shah, 2024. "Joint Component Estimation for Electricity Price Forecasting Using Functional Models," Energies, MDPI, vol. 17(14), pages 1-18, July.
    15. Dai, Zhifeng & Chang, Xiaoming, 2021. "Forecasting stock market volatility: Can the risk aversion measure exert an important role?," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    16. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
    17. Francesco Parola & Marcello Risitano & Marco Ferretti & Eva Panetti, 2017. "The drivers of port competitiveness: a critical review," Transport Reviews, Taylor & Francis Journals, vol. 37(1), pages 116-138, January.
    18. Ivan Kitov & Oleg Kitov, 2013. "Does Banque de France control inflation and unemployment?," Papers 1311.1097, arXiv.org.
    19. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    20. Paul Viefers & Ferdinand Fichtner & Simon Junker & Maximilian Podstawski, 2014. "Filtering German Economic Conditions from a Large Dataset: The New DIW Economic Barometer," Discussion Papers of DIW Berlin 1414, DIW Berlin, German Institute for Economic Research.

    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:infotm:v:18:y:2017:i:3:d:10.1007_s10799-016-0267-3. 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.