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A Generic Modelling Framework for Last-Mile Delivery Systems

Gürcan, Önder, Szczepanska, Timo, Falck, Vanja, Antosz, Patrycja, Cebeci, Merve Seher, de Bok, Michiel, Tapia, Rodrigo, Tavasszy, Lóránt

arXiv.org Artificial Intelligence

Large-scale social digital twinning projects are complex with multiple objectives. For example, a social digital twinning platform for innovative last-mile delivery solutions may aim to assess consumer delivery method choices within their social environment. However, no single tool can achieve all objectives. Different simulators exist for consumer behavior and freight transport. Therefore, we propose a high-level architecture and present a blueprint for a generic modelling framework. This includes defining modules, input/output data, and interconnections, while addressing data suitability and compatibility risks. We demonstrate the framework's effectiveness with two real-world case studies.