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A Crowdsourcing Approach for Sustainable Last Mile Delivery

Author

Listed:
  • Adriana Giret

    (Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València, Camino de Vera s/n. 46022 Valencia, Spain)

  • Carlos Carrascosa

    (Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València, Camino de Vera s/n. 46022 Valencia, Spain)

  • Vicente Julian

    (Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València, Camino de Vera s/n. 46022 Valencia, Spain)

  • Miguel Rebollo

    (Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València, Camino de Vera s/n. 46022 Valencia, Spain)

  • Vicente Botti

    (Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València, Camino de Vera s/n. 46022 Valencia, Spain)

Abstract

Sustainable transportation is one of the major concerns in cities. This concern involves all type of movements motivated by different goals (mobility of citizens, transportation of goods and parcels, etc.). The main goal of this work is to provide an intelligent approach for Sustainable Last Mile Delivery, by reducing (or even deleting) the need of dedicated logistic moves (by cars, and/or trucks). The method attempts to reduce the number of movements originated by the parcels delivery by taking advantage of the citizens’ movements. In this way our proposal follows a crowdsourcing approach, in which the citizens that moves in the city, because of their own needs, become temporal deliverers. The technology behind our approach relays on Multi-agent System techniques and complex network-based algorithms for optimizing sustainable delivery routes. These artificial intelligent approaches help to reduce the complexity of the scenario providing an efficient way to integrate the citizens’ routes that can be executed using the different transportation means and networks available in the city (public system, private transportation, eco-vehicles sharing systems, etc.). A complex network-based algorithm is used for computing and proposing an optimized Sustainable Last Mile Delivery route to the crowd. Moreover, the executed tests show the feasibility of the proposed solution, together with a high reduction of the CO 2 emission coming from the delivery trucks that, in the case studies, are no longer needed for delivery.

Suggested Citation

  • Adriana Giret & Carlos Carrascosa & Vicente Julian & Miguel Rebollo & Vicente Botti, 2018. "A Crowdsourcing Approach for Sustainable Last Mile Delivery," Sustainability, MDPI, vol. 10(12), pages 1-17, December.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:12:p:4563-:d:187435
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    References listed on IDEAS

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    Cited by:

    1. Rocio de la Torre & Canan G. Corlu & Javier Faulin & Bhakti S. Onggo & Angel A. Juan, 2021. "Simulation, Optimization, and Machine Learning in Sustainable Transportation Systems: Models and Applications," Sustainability, MDPI, vol. 13(3), pages 1-21, February.
    2. Tomáš Settey & Jozef Gnap & Dominika Beňová & Michal Pavličko & Oľga Blažeková, 2021. "The Growth of E-Commerce Due to COVID-19 and the Need for Urban Logistics Centers Using Electric Vehicles: Bratislava Case Study," Sustainability, MDPI, vol. 13(10), pages 1-20, May.
    3. Jakob Pohlisch, 2020. "Internal Open Innovation—Lessons Learned from Internal Crowdsourcing at SAP," Sustainability, MDPI, vol. 12(10), pages 1-22, May.
    4. Nima Pourmohammadreza & Mohammad Reza Akbari Jokar, 2023. "A Novel Two-Phase Approach for Optimization of the Last-Mile Delivery Problem with Service Options," Sustainability, MDPI, vol. 15(10), pages 1-25, May.
    5. Kexin Bi & Mengke Yang & Latif Zahid & Xiaoguang Zhou, 2020. "A New Solution for City Distribution to Achieve Environmental Benefits within the Trend of Green Logistics: A Case Study in China," Sustainability, MDPI, vol. 12(20), pages 1-25, October.
    6. Melkonyan, Ani & Gruchmann, Tim & Lohmar, Fabian & Kamath, Vasanth & Spinler, Stefan, 2020. "Sustainability assessment of last-mile logistics and distribution strategies: The case of local food networks," International Journal of Production Economics, Elsevier, vol. 228(C).
    7. Garola, Giovanni & Seghezzi, Arianna & Siragusa, Chiara & Mangiaracina, Riccardo, 2022. "Sustainability in urban logistics: A literature review," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Jahn, Carlos & Blecker, Thorsten & Ringle, Christian M. (ed.), Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management – Innovative Approaches for the Shift to a New , volume 33, pages 709-730, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    8. Bathke, Henrik & Hartmann, Evi, 2021. "Accepting a crowdsourced delivery - A choice-based conjoint analysis," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Adapting to the Future: Maritime and City Logistics in the Context of Digitalization and Sustainability. Proceedings of the Hamburg International Conf, volume 32, pages 65-95, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.

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