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Truck Arrivals Scheduling with Vessel Dependent Time Windows to Reduce Carbon Emissions

Author

Listed:
  • Mengzhi Ma

    (College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China
    Institute of Transportation Strategy Management and Systematic Planning, Dalian Maritime University, Dalian 116026, China)

  • Houming Fan

    (College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China
    Institute of Transportation Strategy Management and Systematic Planning, Dalian Maritime University, Dalian 116026, China)

  • Xiaodan Jiang

    (College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, China)

  • Zhenfeng Guo

    (College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China)

Abstract

Irregular external truck arrivals at a marine container terminal often leads to long queues at gates and substantial greenhouse gas emissions. To relieve gate congestion and reduce carbon emissions, a new truck arrival pattern called “vessel dependent time windows (VDTWs)” is proposed. A two-phase queuing model is established to describe the queuing process of trucks at gate and yard. An optimization model is established to assign time window and appointment quota for each vessel in a marine container terminal running a terminal appointment system (TAS) with VDTWs. The objective is to minimize the total carbon dioxide emissions of trucks and rubber-tired gantry cranes (RTGCs) during idling. The storage capacity constraints of each block and maximum queue length are also taken into consideration. A hybrid genetic algorithm based on simulated annealing is developed to solve the problem. Results based on numerical experiments demonstrate that this model can substantially reduce the waiting time of trucks at gate and yard and carbon dioxide emissions of trucks and RTGCs during idling.

Suggested Citation

  • Mengzhi Ma & Houming Fan & Xiaodan Jiang & Zhenfeng Guo, 2019. "Truck Arrivals Scheduling with Vessel Dependent Time Windows to Reduce Carbon Emissions," Sustainability, MDPI, vol. 11(22), pages 1-26, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:22:p:6410-:d:287014
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    References listed on IDEAS

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    Cited by:

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    2. Neven Grubisic & Tomislav Krljan & Livia Maglić & Siniša Vilke, 2020. "The Microsimulation Model for Assessing the Impact of Inbound Traffic Flows for Container Terminals Located near City Centers," Sustainability, MDPI, vol. 12(22), pages 1-19, November.
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    5. Lange, Ann-Kathrin & Kreuz, Felix & Langkau, Sven & Jahn, Carlos & Clausen, Uwe, 2020. "Defining the quota of truck appointment systems," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Data Science in Maritime and City Logistics: Data-driven Solutions for Logistics and Sustainability. Proceedings of the Hamburg International Conferen, volume 30, pages 211-246, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    6. Lange, Ann-Kathrin & Nellen, Nicole & Jahn, Carlos, 2022. "Truck appointment systems: How can they be improved and what are their limits?," 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 615-655, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    7. Jun-Ho Lee & Hoon Jang, 2019. "Uniform Parallel Machine Scheduling with Dedicated Machines, Job Splitting and Setup Resources," Sustainability, MDPI, vol. 11(24), pages 1-23, December.

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