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A novel dynamic resource allocation model for demand-responsive city logistics distribution operations

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  • Sheu, Jiuh-Biing

Abstract

This paper presents a dynamic customer group-based logistics resource allocation methodology for the use of demand-responsive city logistics distribution operations. The proposed methodology is developed based on the following five developmental procedures, including: (1) specification of demand attributes, (2) customer grouping, (3) customer group ranking, (4) container assignment, and (5) vehicle assignment. The numerical results show that the model permits managing both the time-varying customer order data and logistics resources dynamically with the goal of optimal logistics resource allocation. Particularly, both the aggregate operational costs and average lead time are reduced by 27.4% and 8.7%, respectively, in a case study.

Suggested Citation

  • Sheu, Jiuh-Biing, 2006. "A novel dynamic resource allocation model for demand-responsive city logistics distribution operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 42(6), pages 445-472, November.
    • Handle: RePEc:eee:transe:v:42:y:2006:i:6:p:445-472
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    Citations

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    Cited by:

    1. Mathew, Tom V. & Khasnabis, Snehamay & Mishra, Sabyasachee, 2010. "Optimal resource allocation among transit agencies for fleet management," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(6), pages 418-432, July.
    2. Mishra, Sabyasachee & Sharma, Sushant & Khasnabis, Snehamay & Mathew, Tom V., 2013. "Preserving an aging transit fleet: An optimal resource allocation perspective based on service life and constrained budget," Transportation Research Part A: Policy and Practice, Elsevier, vol. 47(C), pages 111-123.
    3. Ngo, Huan Hoang & Shah, Rohan & Mishra, Sabyasachee, 2018. "Optimal asset management strategies for mixed transit fleet," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 103-116.
    4. Jidong Guo & Qiuhong Zhao & Menghao Xi, 2022. "Sustainable Urban Logistics Distribution Network Planning with Carbon Tax," Sustainability, MDPI, vol. 14(20), pages 1-15, October.
    5. Niu, Yi-Feng & Gao, Zi-You & Lam, William H.K., 2017. "Evaluating the reliability of a stochastic distribution network in terms of minimal cuts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 100(C), pages 75-97.
    6. Tang Zhizhong & Li Bo & Qiu Hongyan, 2015. "A Dynamic Clustering Method to Large-Scale Distribution Problems," Journal of Systems Science and Information, De Gruyter, vol. 3(1), pages 25-36, February.

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