Abstract
Physical Internet (PI) seeks to enhance logistic network performance across the social, financial and environmental domains. PI-Hubs, serving as transhipment facilities within Hyperconnected City Logistics networks, have the objective of transiting incoming goods through the hub in the most efficient manner. Inbound & outbound vehicles are required to be scheduled, incoming goods unloaded from vehicles & reconsolidated within the facility, before being optimally loaded onto outbound vehicles. This study investigates the benefits of utilising enhanced granularity of estimated time of arrivals (ETAs) of vehicles at PI-Hubs, in conjunction with artificial intelligence (AI) for classifying vehicles based on their ETAs. Furthermore, it assesses the impact of utilising GS1’s Scan4Transport Digital Link (GS1DL) in enabling dock reallocations.
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