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Fuzzy Linear Programming Models for a Green Logistics Center Location and Allocation Problem under Mixed Uncertainties Based on Different Carbon Dioxide Emission Reduction Methods

Author

Listed:
  • Yan Sun

    (School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, China)

  • Yue Lu

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Cevin Zhang

    (Unit of Logistics and Informatics, KTH Royal Institute of Technology, 14156 Huddinge, Sweden)

Abstract

This study explores a foundational logistics center location and allocation problem in a three-stage logistics network that consists of suppliers, logistics centers, and customers. In this study, the environmental sustainability of the logistics network is improved by optimizing the carbon dioxide emissions of the logistics network based on multi-objective optimization and carbon tax regulation. Mixed uncertainties in the planning stage, including the supply capacities of suppliers, operation capacities of logistics centers, and demands of customers, are modeled using triangular fuzzy numbers based on the fuzzy set theory to order to enhance the reliability of the logistics center location and allocation planning. To solve the green logistics center location and allocation problem under mixed uncertainties, we establish two fuzzy mixed integer linear programming models. The fuzzy credibilistic chance-constrained programming is then adopted to obtain the crisp and linear reformulations of the fuzzy programming models. A numerical case is given to verify the feasibility of the proposed methods, in which the performance of carbon tax regulation in reducing carbon dioxide emissions is then tested based on the benchmark provided by the multi-objective optimization. Lastly, sensitivity analysis and fuzzy simulation are utilized to reveal the effect of the mixed uncertainties on the logistics location and allocation planning and further determine the best confidence level in the fuzzy chance constraints to provide decision makers with a crisp plan.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:22:p:6448-:d:287600
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    References listed on IDEAS

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    1. Zhitao Xu & Adel Elomri & Shaligram Pokharel & Fatih Mutlu, 2019. "The Design of Green Supply Chains under Carbon Policies: A Literature Review of Quantitative Models," Sustainability, MDPI, vol. 11(11), pages 1-20, May.
    2. Jean-François Cordeau & Federico Pasin & Marius Solomon, 2006. "An integrated model for logistics network design," Annals of Operations Research, Springer, vol. 144(1), pages 59-82, April.
    3. 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.
    4. Lee, Der-Horng & Dong, Meng, 2009. "Dynamic network design for reverse logistics operations under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(1), pages 61-71, January.
    5. 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.
    6. Crainic, Teodor Gabriel, 2000. "Service network design in freight transportation," European Journal of Operational Research, Elsevier, vol. 122(2), pages 272-288, April.
    7. Yan Sun & Martin Hrušovský & Chen Zhang & Maoxiang Lang, 2018. "A Time-Dependent Fuzzy Programming Approach for the Green Multimodal Routing Problem with Rail Service Capacity Uncertainty and Road Traffic Congestion," Complexity, Hindawi, vol. 2018, pages 1-22, June.
    8. Mula, Josefa & Peidro, David & Poler, Raul, 2010. "The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand," International Journal of Production Economics, Elsevier, vol. 128(1), pages 136-143, November.
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    Cited by:

    1. Jiawei Ge & Xiang Li & Zhuoling Wu & Yurou Sun & Maneerat Kanrak, 2022. "The Distribution of Emergency Logistics Centers under the COVID-19 Lockdown: The Case of Yangtze River Delta Area," Sustainability, MDPI, vol. 14(17), pages 1-16, August.
    2. Liang Yuan & Xia Wu & Weijun He & Yang Kong & Thomas Stephen Ramsey & Dagmawi Mulugeta Degefu, 2022. "A multi-weight fuzzy Methodological Framework for Allocating Coalition Payoffs of Joint Water Environment Governance in Transboundary River Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3367-3384, July.

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