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A Decision Support System for Optimizing the Last-Mile by Mixing Traditional and Green Logistics

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Information Systems, Logistics, and Supply Chain (ILS 2016)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 262))

Abstract

In recent years, freight transportation has emerged as a key factor in the development and dynamicity of countries. However, it has had a considerable impact on urban areas due to environmental issues. Owing to this, several stakeholders have implemented City Logistics solutions in order to make freight transportation more sustainable and efficient. This paper proposes a case study focused on a collaborative transportation system involving traditional and green couriers in the city of Turin (Italy). This freight pooling is sustained by a decision support system that combines the ERP “Odoo” with an algorithm for route optimization planning. The development of this decision support system is based on GUEST methodology, which begins with the identification of the green courier needs and requirements and extends to the implementation of the overall solution. Finally, several results obtained from the application of the decision support system are discussed.

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Acknowledgments

Partial funding for this project was provided by the Italian University and Research Ministry under the UrbeLOG project - Smart Cities and Communities and the Natural Sciences and Engineering Council of Canada (NSERC) through its Discovery Grant program.

We want to thank the PonyZero S.r.l. management, and Marco Actis and Alessandro Mohammadi in particular, for their support during this project. The authors are grateful to Marko Bagaric and Luca Gobbato for their contributions to a previous version of this work.

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Correspondence to Mariangela Rosano .

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Perboli, G., Rosano, M. (2018). A Decision Support System for Optimizing the Last-Mile by Mixing Traditional and Green Logistics. In: Temponi, C., Vandaele, N. (eds) Information Systems, Logistics, and Supply Chain. ILS 2016. Lecture Notes in Business Information Processing, vol 262. Springer, Cham. https://doi.org/10.1007/978-3-319-73758-4_3

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  • DOI: https://doi.org/10.1007/978-3-319-73758-4_3

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