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Building a collaborative solution in dense urban city settings to enhance parcel delivery: An effective crowd model in Paris

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  • Akeb, Hakim
  • Moncef, Btissam
  • Durand, Bruno

Abstract

Parcel delivery, one of the major components of e-commerce growth, has become increasingly challenging with the increase of urban logistics issues. During home delivery, many parcels may be returned if the customer is away from home, generating additional costs. In this paper, we propose a solution based on the crowd that consists to collect and deliver parcels using individuals (neighbors). The method uses circle packing to estimate the number of neighbors needed, the number of parcels they have to manage and the corresponding reward. An experiment was conducted on the 12th district of Paris (France) dataset. Encouraging results were obtained.

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  • Akeb, Hakim & Moncef, Btissam & Durand, Bruno, 2018. "Building a collaborative solution in dense urban city settings to enhance parcel delivery: An effective crowd model in Paris," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 119(C), pages 223-233.
    • Handle: RePEc:eee:transe:v:119:y:2018:i:c:p:223-233
    DOI: 10.1016/j.tre.2018.04.007
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    4. Yuen, Kum Fai & Wang, Xueqin & Ma, Fei & Wong, Yiik Diew, 2019. "The determinants of customers’ intention to use smart lockers for last-mile deliveries," Journal of Retailing and Consumer Services, Elsevier, vol. 49(C), pages 316-326.
    5. Boysen, Nils & Emde, Simon & Schwerdfeger, Stefan, 2022. "Crowdshipping by employees of distribution centers: Optimization approaches for matching supply and demand," European Journal of Operational Research, Elsevier, vol. 296(2), pages 539-556.
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    7. Mommens, Koen & Buldeo Rai, Heleen & van Lier, Tom & Macharis, Cathy, 2021. "Delivery to homes or collection points? A sustainability analysis for urban, urbanised and rural areas in Belgium," Journal of Transport Geography, Elsevier, vol. 94(C).
    8. Shao, Saijun & Xu, Gangyan & Li, Ming & Huang, George Q., 2019. "Synchronizing e-commerce city logistics with sliding time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 123(C), pages 17-28.
    9. Gläser, Sina, 2022. "A waste collection problem with service type option," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1216-1230.
    10. Yang, Fei & Dai, Ying & Ma, Zu-Jun, 2020. "A cooperative rich vehicle routing problem in the last-mile logistics industry in rural areas," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    11. Wang, Haibo & Alidaee, Bahram, 2023. "White-glove service delivery: A quantitative analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    12. Xiao, Fei & Wang, Haijun & Guo, Shuojia & Guan, Xu & Liu, Baoshan, 2021. "Efficient and truthful multi-attribute auctions for crowdsourced delivery," International Journal of Production Economics, Elsevier, vol. 240(C).
    13. Pourrahmani, Elham & Jaller, Miguel, 2021. "Crowdshipping in last mile deliveries: Operational challenges and research opportunities," Socio-Economic Planning Sciences, Elsevier, vol. 78(C).
    14. Pahwa, Anmol & Jaller, Miguel, 2022. "A cost-based comparative analysis of different last-mile strategies for e-commerce delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    15. Pahwa, Anmol & Jaller, Miguel, 2023. "Assessing last-mile distribution resilience under demand disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 172(C).
    16. Verheyen, Wouter & Kołacz, Marta K., 2022. "Enhancing safety in B2C delivery chains," Transport Policy, Elsevier, vol. 117(C), pages 12-22.
    17. Chu, Xiang & Liu, Jun & Ren, Long & Gong, Daqing, 2020. "Optimal contract design with a common agency in last-mile logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 139(C).

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