Highlights A comprehensive framework is proposed to manage trucks operations within a TAS. A clustering approach groups export and import container to reduce empty truck trips. An optimization model is proposed to improve truck service levels. An extensive Design of Experiments analysis is performed to validate the approach. Real case studies related to terminals in Mexico and Italy are tested and analyzed.

    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.


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    Title :

    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


    Contributors:


    Publication date :

    2020-07-26




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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