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Modeling city logistics using adaptive dynamic programming based multi-agent simulation

Author

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  • Firdausiyah, N.
  • Taniguchi, E.
  • Qureshi, A.G.

Abstract

The effects of city logistics solutions are uncertain due to fluctuating demand, parking issues and multiple agents within the system. This research modelled the behavior of freight carriers and an Urban Consolidation Center (UCC) operator using Multi-Agent Simulation-Adaptive Dynamic Programming based Reinforcement Learning (MAS-ADP based RL) to evaluate a Joint Delivery Systems in an uncertain environment. The MAS-ADP based RL is superior to MAS-Q-learning in replicating the potential actions of the agents under uncertain environment by adapting to the changing environment properly into accurate decisions thus increasing the accuracy of agent’s decision making and eventually reducing environmental emissions as well.

Suggested Citation

  • Firdausiyah, N. & Taniguchi, E. & Qureshi, A.G., 2019. "Modeling city logistics using adaptive dynamic programming based multi-agent simulation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 74-96.
    • Handle: RePEc:eee:transe:v:125:y:2019:i:c:p:74-96
    DOI: 10.1016/j.tre.2019.02.011
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    References listed on IDEAS

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    Cited by:

    1. Liu, Sijing & He, Nannan & Cao, Xindan & Li, Guoqi & Jian, Ming, 2022. "Logistics cluster and its future development: A comprehensive research review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
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    3. Kong, Xiang T.R. & Kang, Kai & Zhong, Ray Y. & Luo, Hao & Xu, Su Xiu, 2021. "Cyber physical system-enabled on-demand logistics trading," International Journal of Production Economics, Elsevier, vol. 233(C).
    4. Jamal Alnsour & Abdullah Radwan Arabeyyat & Khalil Al-Hyari & Sakher A. I. Al-Bazaiah & Reeman Aldweik, 2023. "Enhancing City Logistics for Sustainable Development in Jordan: A Survey-Based Study," Logistics, MDPI, vol. 8(1), pages 1-14, December.
    5. Amaya, Johanna & Delgado-Lindeman, Maira & Arellana, Julian & Allen, Jaime, 2021. "Urban freight logistics: What do citizens perceive?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    6. Regal, Andrés & Gonzalez-Feliu, Jesús & Rodriguez, Michelle, 2023. "A spatio-functional logistics profile clustering analysis method for metropolitan areas," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    7. Rémy Dupas & Eiichi Taniguchi & Jean-Christophe Deschamps & Ali G. Qureshi, 2020. "A Multi-commodity Network Flow Model for Sustainable Performance Evaluation in City Logistics: Application to the Distribution of Multi-tenant Buildings in Tokyo," Sustainability, MDPI, vol. 12(6), pages 1-18, March.
    8. Li, Feng & Du, Timon C. & Wei, Ying, 2020. "Enhancing supply chain decisions with consumers’ behavioral factors: An illustration of decoy effect," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    9. Justyna Winkowska & Danuta Szpilko, 2020. "Methodology for Integration of Smart City Dimensions in the Socialised Process of Creating City Development," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 524-547.
    10. Krystian Pietrzak & Oliwia Pietrzak & Andrzej Montwiłł, 2021. "Effects of Incorporating Rail Transport into a Zero-Emission Urban Deliveries System: Application of Light Freight Railway (LFR) Electric Trains," Energies, MDPI, vol. 14(20), pages 1-24, October.
    11. Yan, Yimo & Chow, Andy H.F. & Ho, Chin Pang & Kuo, Yong-Hong & Wu, Qihao & Ying, Chengshuo, 2022. "Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 162(C).
    12. Na Zhang & Xiangxiang Zhang & Yingjie Yang, 2019. "The Behavior Mechanism of the Urban Joint Distribution Alliance under Government Supervision from the Perspective of Sustainable Development," Sustainability, MDPI, vol. 11(22), pages 1-20, November.
    13. Utomo, D.S. & Gripton, A. & Greening, P., 2021. "Analysing charging strategies for electric LGV in grocery delivery operation using agent-based modelling: An initial case study in the United Kingdom," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 148(C).
    14. Xin Yao & Yuanyuan Cheng & Li Zhou & Malin Song, 2022. "Green efficiency performance analysis of the logistics industry in China: based on a kind of machine learning methods," Annals of Operations Research, Springer, vol. 308(1), pages 727-752, January.
    15. Sergio Maria Patella & Gianluca Grazieschi & Valerio Gatta & Edoardo Marcucci & Stefano Carrese, 2020. "The Adoption of Green Vehicles in Last Mile Logistics: A Systematic Review," Sustainability, MDPI, vol. 13(1), pages 1-29, December.
    16. Comi, Antonio, 2020. "A modelling framework to forecast urban goods flows," Research in Transportation Economics, Elsevier, vol. 80(C).
    17. Shuangyan Li & Yijing Liang & Zhenjie Wang & Dezhi Zhang, 2021. "An Optimization Model of a Sustainable City Logistics Network Design Based on Goal Programming," Sustainability, MDPI, vol. 13(13), pages 1-20, July.
    18. Sayarshad, Hamid R. & Sattar, Shahram & Oliver Gao, H., 2020. "A scalable non-myopic atomic game for a smart parking mechanism," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).
    19. Bahareh Mansouri & Subhasmita Sahu & M. Ali Ülkü, 2023. "Toward Greening City Logistics: A Systematic Review on Corporate Governance and Social Responsibility in Managing Urban Distribution Centers," Logistics, MDPI, vol. 7(1), pages 1-20, March.
    20. Kim, Nayeon & Montreuil, Benoit & Klibi, Walid & Kholgade, Nitish, 2021. "Hyperconnected urban fulfillment and delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).

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