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Multi-objective inter-terminal truck routing

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  • Heilig, Leonard
  • Lalla-Ruiz, Eduardo
  • Voß, Stefan

Abstract

A tremendous growth in containerized trade has led to severe traffic problems in urban port areas associated with negative effects on both efficiency and pollution. In this paper, we address the multi-objective inter-terminal truck routing problem by specifically considering truck emissions. We present an efficient multi-objective archived simulated annealing approach and a visualization technique. Managerial insights are discussed based on an analysis of trade-offs. The algorithm is embedded in a cloud-based decision support system to consider contextual data, including traffic data and the current positions of trucks. This allows ports to utilize potentials of digitalization and optimization while addressing environmental issues.

Suggested Citation

  • Heilig, Leonard & Lalla-Ruiz, Eduardo & Voß, Stefan, 2017. "Multi-objective inter-terminal truck routing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 178-202.
    • Handle: RePEc:eee:transe:v:106:y:2017:i:c:p:178-202
    DOI: 10.1016/j.tre.2017.07.008
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    4. Amir Gharehgozli & Nima Zaerpour & Rene Koster, 2020. "Container terminal layout design: transition and future," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 22(4), pages 610-639, December.
    5. Mar-Ortiz, Julio & Castillo-García, Norberto & Gracia, María D., 2020. "A decision support system for a capacity management problem at a container terminal," International Journal of Production Economics, Elsevier, vol. 222(C).
    6. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2023. "Industry 5.0 and Triple Bottom Line Approach in Supply Chain Management: The State-of-the-Art," Sustainability, MDPI, vol. 15(7), pages 1-30, March.
    7. Ahmed Zainul Abideen & Veera Pandiyan Kaliani Sundram & Jaafar Pyeman & Abdul Kadir Othman & Shahryar Sorooshian, 2021. "Digital Twin Integrated Reinforced Learning in Supply Chain and Logistics," Logistics, MDPI, vol. 5(4), pages 1-22, November.
    8. Houming Fan & Xiaoxue Ren & Zhenfeng Guo & Yang Li, 2019. "Truck Scheduling Problem Considering Carbon Emissions under Truck Appointment System," Sustainability, MDPI, vol. 11(22), pages 1-23, November.
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