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A combined data mining – optimization approach to manage trucks operations in container terminals with the use of a TAS: Application to an Italian and a Mexican port

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  • Caballini, Claudia
  • Gracia, Maria D.
  • Mar-Ortiz, Julio
  • Sacone, Simona

Abstract

This paper relates with the assignment of trucks to time slots in container terminals equipped with Truck Appointment Systems. A two-phase approach is provided: first, export and import containers are matched in tuples with a clustering analysis to reduce the number of empty trips and, then, tuples are assigned to time slots to minimize trucks deviation from their preferred time slots and truck turnaround times. Real case instances related to Mexican and Italian container terminals are tested. Results show that our approach reduces empty-truck trips up to 33.79% and that it can be successfully applied to any container terminal.

Suggested Citation

  • Caballini, Claudia & Gracia, Maria D. & Mar-Ortiz, Julio & Sacone, Simona, 2020. "A combined data mining – optimization approach to manage trucks operations in container terminals with the use of a TAS: Application to an Italian and a Mexican port," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    • Handle: RePEc:eee:transe:v:142:y:2020:i:c:s1366554520307055
    DOI: 10.1016/j.tre.2020.102054
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    1. Pierre J. Dejax & Teodor Gabriel Crainic, 1987. "Survey Paper---A Review of Empty Flows and Fleet Management Models in Freight Transportation," Transportation Science, INFORMS, vol. 21(4), pages 227-248, November.
    2. Corne, David & Dhaenens, Clarisse & Jourdan, Laetitia, 2012. "Synergies between operations research and data mining: The emerging use of multi-objective approaches," European Journal of Operational Research, Elsevier, vol. 221(3), pages 469-479.
    3. Xiaoju Zhang & Qingcheng Zeng & Zhongzhen Yang, 2019. "Optimization of truck appointments in container terminals," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(1), pages 125-145, March.
    4. Gang Chen & Zhongzhen Yang, 2010. "Optimizing time windows for managing export container arrivals at Chinese container terminals," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 12(1), pages 111-126, March.
    5. Phan, Mai-Ha & Kim, Kap Hwan, 2016. "Collaborative truck scheduling and appointments for trucking companies and container terminals," Transportation Research Part B: Methodological, Elsevier, vol. 86(C), pages 37-50.
    6. Schulte, Frederik & Lalla-Ruiz, Eduardo & González-Ramírez, Rosa G. & Voß, Stefan, 2017. "Reducing port-related empty truck emissions: A mathematical approach for truck appointments with collaboration," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 195-212.
    7. Yin, Yong & Yasuda, Kazuhiko, 2006. "Similarity coefficient methods applied to the cell formation problem: A taxonomy and review," International Journal of Production Economics, Elsevier, vol. 101(2), pages 329-352, June.
    8. Olafsson, Sigurdur & Li, Xiaonan & Wu, Shuning, 2008. "Operations research and data mining," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1429-1448, June.
    9. Chen, Gang & Govindan, Kannan & Golias, Mihalis M., 2013. "Reducing truck emissions at container terminals in a low carbon economy: Proposal of a queueing-based bi-objective model for optimizing truck arrival pattern," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 55(C), pages 3-22.
    10. Meisel, Stephan & Mattfeld, Dirk, 2010. "Synergies of Operations Research and Data Mining," European Journal of Operational Research, Elsevier, vol. 206(1), pages 1-10, October.
    11. Zehendner, Elisabeth & Feillet, Dominique, 2014. "Benefits of a truck appointment system on the service quality of inland transport modes at a multimodal container terminal," European Journal of Operational Research, Elsevier, vol. 235(2), pages 461-469.
    12. Phan, Mai-Ha & Kim, Kap Hwan, 2015. "Negotiating truck arrival times among trucking companies and a container terminal," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 75(C), pages 132-144.
    13. Chen, Xiaoming & Zhou, Xuesong & List, George F., 2011. "Using time-varying tolls to optimize truck arrivals at ports," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 965-982.
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    Cited by:

    1. Liu, Sijing & He, Nannan & Cao, Xindan & Li, Guoqi & Jian, Ming, 2022. "Logistics cluster and its future development: A comprehensive research review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    2. Azab, Ahmed & Morita, Hiroshi, 2022. "Coordinating truck appointments with container relocations and retrievals in container terminals under partial appointments information," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    3. Raeesi, Ramin & Sahebjamnia, Navid & Mansouri, S. Afshin, 2023. "The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 943-973.
    4. 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).

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