An Artificial Intelligence-based software module for the optimization of collaborative delivery in last-mile logistics
Author(s)
Fajardo-Calderin, Jenny
Masegosa, Antonio D.
Elejoste, Maria Pilar
Moreno-Emborujo, Asier
Cantero-Lopez, Xabier
Angulo, Ignacio
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Abstract
This Paper presents a route delivery planning and simulation module that forms a core part of the ICT Platform of the H2020 SENATOR project, which aims to enhance the sustainability of cities by developing a new urban logistic model. The module utilizes AI-based optimization algorithms to support the matching of supply and demand, identify the best fleet mix, and estimate the best delivery route based on real-time conditions. It also allows last-mile delivery planning using different transport modes, inter-modality, and driving restrictions, and simultaneously optimizes different performance indicators. The Paper provides a detailed description of the AI-based optimization method and the architecture and components of the software module. Finally, the software module is validated in two scenarios (current operations and implementation of a Low Emission Zone) using real shipment data from a postal operator company in the living lab that the SENATOR project is implementing in Zaragoza.
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Date
2023-06
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Paper