Design of flexible truck appointment system based on machine learning approach

Maurício Randolfo Flores da Silva, Enzo Morosini Frazzon and Vanina Macowski Durski Silva

International Journal of Logistics Systems and Management, 2024, vol. 48, issue 2, 244-266

Abstract: Smart ports are adopting Industry 4.0 concepts and technologies so that more efficient and resilient operations emerge. Real-time data acquired from smart technologies can be deployed to anticipate disruptions and to actively manage hinterland port flows. In this context, the flexible rescheduling of truck flows in response to unpredictable circumstances allows for congestion mitigation and reduced cycle time. This paper investigates the literature regarding smart ports, scheduling methods, and machine learning approaches, in order to propose a conceptual model for flexible truck appointment systems, able to consider a continuous stream of real-time data from smart technologies to identify disruptive events and to dynamically reschedule truck appointments, ensuring the synchronisation of hinterland port truck flows.

Keywords: smart ports; smart logistics system; truck appointment system; TAS; flexible schedule; machine learning. (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=139958 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijlsma:v:48:y:2024:i:2:p:244-266

Access Statistics for this article

More articles in International Journal of Logistics Systems and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().