Title: Design of flexible truck appointment system based on machine learning approach

Authors: Maurício Randolfo Flores da Silva; Enzo Morosini Frazzon; Vanina Macowski Durski Silva

Addresses: Department of Production Engineering, Technology Centre, Federal University of Santa Catarina, Florianópolis, Brazil ' Department of Production Engineering, Technology Centre, Federal University of Santa Catarina, Florianópolis, Brazil ' Department of Mobility Engineering, Technology Centre, Federal University of Santa Catarina, Joinville, Brazil

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.

DOI: 10.1504/IJLSM.2024.139958

International Journal of Logistics Systems and Management, 2024 Vol.48 No.2, pp.244 - 266

Received: 01 Jun 2021
Accepted: 21 Nov 2021
Published online: 15 Jul 2024 *

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