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A multimodal logistics service network design with time windows and environmental concerns

Author

Listed:
  • Dezhi Zhang
  • Runzhong He
  • Shuangyan Li
  • Zhongwei Wang

Abstract

The design of a multimodal logistics service network with customer service time windows and environmental costs is an important and challenging issue. Accordingly, this work established a model to minimize the total cost of multimodal logistics service network design with time windows and environmental concerns. The proposed model incorporates CO2 emission costs to determine the optimal transportation mode combinations and investment selections for transfer nodes, which consider transport cost, transport time, carbon emission, and logistics service time window constraints. Furthermore, genetic and heuristic algorithms are proposed to set up the abovementioned optimal model. A numerical example is provided to validate the model and the abovementioned two algorithms. Then, comparisons of the performance of the two algorithms are provided. Finally, this work investigates the effects of the logistics service time windows and CO2 emission taxes on the optimal solution. Several important management insights are obtained.

Suggested Citation

  • Dezhi Zhang & Runzhong He & Shuangyan Li & Zhongwei Wang, 2017. "A multimodal logistics service network design with time windows and environmental concerns," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-19, September.
    • Handle: RePEc:plo:pone00:0185001
    DOI: 10.1371/journal.pone.0185001
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    References listed on IDEAS

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    1. Liotta, Giacomo & Stecca, Giuseppe & Kaihara, Toshiya, 2015. "Optimisation of freight flows and sourcing in sustainable production and transportation networks," International Journal of Production Economics, Elsevier, vol. 164(C), pages 351-365.
    2. SteadieSeifi, M. & Dellaert, N.P. & Nuijten, W. & Van Woensel, T. & Raoufi, R., 2014. "Multimodal freight transportation planning: A literature review," European Journal of Operational Research, Elsevier, vol. 233(1), pages 1-15.
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    Citations

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    Cited by:

    1. Kai Kang & Wei Pu & Yanfang Ma & Xiaoyu Wang, 2018. "Bi-objective inventory allocation planning problem with supplier selection and carbon trading under uncertainty," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-25, November.
    2. Dezhi Zhang & Xin Wang & Shuangyan Li & Nan Ni & Zhuo Zhang, 2018. "Joint optimization of green vehicle scheduling and routing problem with time-varying speeds," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-20, February.
    3. Noorliza Karia, 2022. "Antecedents and Consequences of Environmental Capability towards Sustainability and Competitiveness," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    4. Yan Sun & Yue Lu & Cevin Zhang, 2019. "Fuzzy Linear Programming Models for a Green Logistics Center Location and Allocation Problem under Mixed Uncertainties Based on Different Carbon Dioxide Emission Reduction Methods," Sustainability, MDPI, vol. 11(22), pages 1-24, November.
    5. K. Noorliza, 2023. "Determinants of an Environmentally Sustainable Model for Competitiveness," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    6. Rizwan Shoukat, 2023. "Multimodal or intermodal: greenhouse gas emissions in less than container load in China–Pakistan trade," Environment Systems and Decisions, Springer, vol. 43(2), pages 265-280, June.
    7. Yan Sun & Xinya Li & Xia Liang & Cevin Zhang, 2019. "A Bi-Objective Fuzzy Credibilistic Chance-Constrained Programming Approach for the Hazardous Materials Road-Rail Multimodal Routing Problem under Uncertainty and Sustainability," Sustainability, MDPI, vol. 11(9), pages 1-27, May.
    8. Yan Sun & Xinya Li, 2019. "Fuzzy Programming Approaches for Modeling a Customer-Centred Freight Routing Problem in the Road-Rail Intermodal Hub-and-Spoke Network with Fuzzy Soft Time Windows and Multiple Sources of Time Uncerta," Mathematics, MDPI, vol. 7(8), pages 1-40, August.
    9. Archetti, Claudia & Peirano, Lorenzo & Speranza, M. Grazia, 2022. "Optimization in multimodal freight transportation problems: A Survey," European Journal of Operational Research, Elsevier, vol. 299(1), pages 1-20.

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