<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Urban logistics plays a crucial role in modern society by covering all the flows of goods and services in the transportation world. This study aims to compare different delivery distribution scenarios using the aggregation of stops by grouping entities, for instance, the postal code approach (PCA) and the volume-based approach (VBA), to indicate the most effective one in simplifying urban logistic operations. These two scenarios illustrate two aggregation criteria: geographical, which groups stops with those closest ones, and non-geographical, which tends to cluster stops with similar stops. Used stops came from a real-world dataset acquired from urban logistics operator in the East of Rome. This study uses an optimizing algorithm called Traveling Salesman Problem (TSP) and Google Matrix API to calculate the shortest path and travel time. A comparison of those two approaches has been made to illustrate the similarities and differences in CO<sub>2</sub> emissions, travel length, travel time, and unloading time. Although PCA is influenced by demand level, results show that PCA leads to a shorter travel time, shorter travel length, and less emissions produced. Furthermore, VBA is a more heterogenous distribution while PCA contains more homogeneity. The outcome could have the potential for companies and researchers interested in urban logistics due to the proposal of a new way of making distributions, real-world data usage, and comparing different scenarios.</p>
</abstract>

Urban logistics plays a crucial role in modern society by covering all the flows of goods and services in the transportation world. This study aims to compare different delivery distribution scenarios using the aggregation of stops by grouping entities, for instance, the postal code approach (PCA) and the volume-based approach (VBA), to indicate the most effective one in simplifying urban logistic operations. These two scenarios illustrate two aggregation criteria: geographical, which groups stops with those closest ones, and non-geographical, which tends to cluster stops with similar stops. Used stops came from a real-world dataset acquired from urban logistics operator in the East of Rome. This study uses an optimizing algorithm called Traveling Salesman Problem (TSP) and Google Matrix API to calculate the shortest path and travel time. A comparison of those two approaches has been made to illustrate the similarities and differences in CO2 emissions, travel length, travel time, and unloading time. Although PCA is influenced by demand level, results show that PCA leads to a shorter travel time, shorter travel length, and less emissions produced. Furthermore, VBA is a more heterogenous distribution while PCA contains more homogeneity. The outcome could have the potential for companies and researchers interested in urban logistics due to the proposal of a new way of making distributions, real-world data usage, and comparing different scenarios.

Analysing Distribution Approaches for Efficient Urban Logistics

lorymichelle.brescianimiristice@uniroma1.it

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Analysing Distribution Approaches for Efficient Urban Logistics"}

Urban logistics plays a crucial role in modern society by covering all the flows of goods and services in the transportation world. This study aims to compare...

Analysing Distribution Approaches for Efficient Urban Logistics

Online veröffentlicht: 17. Nov. 2023

Seitenbereich: 483 - 491

DOI: https://doi.org/10.2478/ttj-2023-0038

Schlüsselwörter
Urban logistics, Last mile logistics, Distribution scenarios, Optimization algorithm

© 2023 Salar Salehi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Analysing Distribution Approaches for Efficient Urban Logistics

Online veröffentlicht: 17. Nov. 2023

Seitenbereich: 483 - 491

DOI: https://doi.org/10.2478/ttj-2023-0038

SchlüsselwörterUrban logistics, Last mile logistics, Distribution scenarios, Optimization algorithm

© 2023 Salar Salehi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Urban logistics, Last mile logistics, Distribution scenarios, Optimization algorithm