IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v179y2024ics0965856423003300.html
   My bibliography  Save this article

Optimal policy for scheduling automated guided vehicles in large-scale intelligent transportation systems

Author

Listed:
  • Wang, Huiwen
  • Yi, Wen
  • Zhen, Lu

Abstract

Automated electric mobility technologies have been increasingly applied to large-scale intelligent transportation systems (ITSs) to enhance productivity and efficiency. Advanced technologies have reshaped the traditional transportation systems and posed numerous challenges to the deployment and management of new ITSs. A major challenge in the real-life implementation of ITSs is how to manage a large number of automated objects in a cooperative manner. In this paper, we investigate the scheduling and routing problem of automated guided vehicles (AGVs) in a complicated ITS. Cost and efficiency are identified as the two crucial performance indicators of such a novel ITS. An easy-to-implement practical decision policy and a tailored particle swarm based solution method are designed for problem solving. In addition to the theoretical contributions, this paper also conducts a case study to validate the effectiveness and applicability of the proposed methodology, thus contributing to the planning and management of large-scale transportation systems by modeling, optimizing, and validating a new ITS deployed with AGVs.

Suggested Citation

  • Wang, Huiwen & Yi, Wen & Zhen, Lu, 2024. "Optimal policy for scheduling automated guided vehicles in large-scale intelligent transportation systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    • Handle: RePEc:eee:transa:v:179:y:2024:i:c:s0965856423003300
    DOI: 10.1016/j.tra.2023.103910
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856423003300
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tra.2023.103910?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ahmed, Gamil & Sheltami, Tarek & Mahmoud, Ashraf & Yasar, Ansar, 2020. "IoD swarms collision avoidance via improved particle swarm optimization," Transportation Research Part A: Policy and Practice, Elsevier, vol. 142(C), pages 260-278.
    2. Outay, Fatma & Mengash, Hanan Abdullah & Adnan, Muhammad, 2020. "Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 116-129.
    3. Xing, Zheng & Liu, Haitao & Wang, Tingsong & Chew, Ek Peng & Lee, Loo Hay & Tan, Kok Choon, 2023. "Integrated automated guided vehicle dispatching and equipment scheduling with speed optimization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    4. van Cranenburgh, Sander & Rose, John M. & Chorus, Caspar G., 2018. "On the robustness of efficient experimental designs towards the underlying decision rule," Transportation Research Part A: Policy and Practice, Elsevier, vol. 109(C), pages 50-64.
    5. Chen Chen & Lee Kong Tiong, 2019. "Using queuing theory and simulated annealing to design the facility layout in an AGV-based modular manufacturing system," International Journal of Production Research, Taylor & Francis Journals, vol. 57(17), pages 5538-5555, September.
    6. King, Barry & Goycoolea, Marcos & Newman, A., 2017. "Optimizing the open pit-to-underground mining transition," European Journal of Operational Research, Elsevier, vol. 257(1), pages 297-309.
    7. Zheyi Tan & Haolin Li & Huiwen Wang & Qiyuan Qian, 2021. "Maritime Container Shipping Fleet Deployment Considering Demand Uncertainty," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 38(03), pages 1-25, June.
    8. Huang, Di & Chen, Xinyuan & Liu, Zhiyuan & Lyu, Cheng & Wang, Shuaian & Chen, Xuewu, 2020. "A static bike repositioning model in a hub-and-spoke network framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    9. Wang, Shuaian & Yan, Ran, 2023. "Fundamental challenge and solution methods in prescriptive analytics for freight transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    10. Kamali, Mohammad & Hewage, Kasun, 2016. "Life cycle performance of modular buildings: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 1171-1183.
    11. Tu Peng & Xu Yang & Zi Xu & Yu Liang, 2020. "Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods," Sustainability, MDPI, vol. 12(19), pages 1-19, October.
    12. Qing Nie & Songyun Liu & Qiyuan Qian & Zheyi Tan & Huiwen Wang, 2021. "Optimization of the Sino-Europe Transport Networks Under Uncertain Demand," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 38(03), pages 1-29, June.
    13. Yan, Ran & Wang, Shuaian & Psaraftis, Harilaos N., 2021. "Data analytics for fuel consumption management in maritime transportation: Status and perspectives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Zhijun & Fei, Jiangang & Du, Yuquan & Ong, Kok-Leong & Arisian, Sobhan, 2024. "A near real-time carbon accounting framework for the decarbonization of maritime transport," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 191(C).
    2. van Cranenburgh, Sander & Bliemer, Michiel C.J., 2019. "Information theoretic-based sampling of observations," Journal of choice modelling, Elsevier, vol. 31(C), pages 181-197.
    3. Xinyu Li & Yi Zuo & Junhao Jiang, 2022. "Application of Regression Analysis Using Broad Learning System for Time-Series Forecast of Ship Fuel Consumption," Sustainability, MDPI, vol. 15(1), pages 1-21, December.
    4. Chris Turner & John Oyekan & Lampros K. Stergioulas, 2021. "Distributed Manufacturing: A New Digital Framework for Sustainable Modular Construction," Sustainability, MDPI, vol. 13(3), pages 1-16, February.
    5. Lei Jiang & Zhongfu Li & Long Li & Yunli Gao, 2018. "Constraints on the Promotion of Prefabricated Construction in China," Sustainability, MDPI, vol. 10(7), pages 1, July.
    6. Pei Dang & Zhanwen Niu & Shang Gao & Lei Hou & Guomin Zhang, 2020. "Critical Factors Influencing the Sustainable Construction Capability in Prefabrication of Chinese Construction Enterprises," Sustainability, MDPI, vol. 12(21), pages 1-21, October.
    7. Kim, Rakhyun & Tae, Sungho & Roh, Seungjun, 2017. "Development of low carbon durability design for green apartment buildings in South Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 263-272.
    8. Nguyen, Son & Fu, Xiuju & Ogawa, Daichi & Zheng, Qin, 2023. "An application-oriented testing regime and multi-ship predictive modeling for vessel fuel consumption prediction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    9. Liu, Yang & Xie, Jiaohong & Chen, Nan, 2022. "Stochastic one-way carsharing systems with dynamic relocation incentives through preference learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    10. Dietz, Annelore & Vera, Sergio & Bustamante, Waldo & Flamant, Gilles, 2020. "Multi-objective optimization to balance thermal comfort and energy use in a mining camp located in the Andes Mountains at high altitude," Energy, Elsevier, vol. 199(C).
    11. Yu, Sisi & Liu, Yanfeng & Wang, Dengjia & Bahaj, AbuBakr S. & Wu, Yue & Liu, Jiaping, 2021. "Review of thermal and environmental performance of prefabricated buildings: Implications to emission reductions in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    12. López-Guerrero, Rafael E. & Vera, Sergio & Carpio, Manuel, 2022. "A quantitative and qualitative evaluation of the sustainability of industrialised building systems: A bibliographic review and analysis of case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    13. Ghosh, Indranil & De, Arijit, 2024. "Maritime Fuel Price Prediction of European Ports using Least Square Boosting and Facebook Prophet: Additional Insights from Explainable Artificial Intelligence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 189(C).
    14. Roope Husgafvel & Daishi Sakaguchi, 2021. "Circular Economy Development in the Construction Sector in Japan," World, MDPI, vol. 3(1), pages 1-26, December.
    15. Han, Peixiu & Liu, Zhongbo & Li, Chi & Sun, Zhuo & Yan, Chunxin, 2024. "A novel federated learning-based two-stage approach for ship energy consumption optimization considering both shipping data security and statistical heterogeneity," Energy, Elsevier, vol. 309(C).
    16. Sikai Chen & Shuya Zong & Tiantian Chen & Zilin Huang & Yanshen Chen & Samuel Labi, 2023. "A Taxonomy for Autonomous Vehicles Considering Ambient Road Infrastructure," Sustainability, MDPI, vol. 15(14), pages 1-27, July.
    17. Geržinič, Nejc & van Cranenburgh, Sander & Cats, Oded & Lancsar, Emily & Chorus, Caspar, 2021. "Estimating decision rule differences between ‘best’ and ‘worst’ choices in a sequential best worst discrete choice experiment," Journal of choice modelling, Elsevier, vol. 41(C).
    18. Nesbitt, Peter & Blake, Lewis R. & Lamas, Patricio & Goycoolea, Marcos & Pagnoncelli, Bernardo K. & Newman, Alexandra & Brickey, Andrea, 2021. "Underground mine scheduling under uncertainty," European Journal of Operational Research, Elsevier, vol. 294(1), pages 340-352.
    19. Andrew Agapiou, 2021. "An Exploration of the Best Value Perceptions of Small Housebuilding Developers towards Offsite Construction," Sustainability, MDPI, vol. 13(7), pages 1-19, April.
    20. Aleksandra Kuzior & Dariusz Krawczyk & Paulina Brożek & Olena Pakhnenko & Tetyana Vasylieva & Serhiy Lyeonov, 2022. "Resilience of Smart Cities to the Consequences of the COVID-19 Pandemic in the Context of Sustainable Development," Sustainability, MDPI, vol. 14(19), pages 1-22, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transa:v:179:y:2024:i:c:s0965856423003300. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.