Cluster Computing

, Volume 22, Supplement 6, pp 13827–13833 | Cite as

Logistics supply chain management based on business ecology theory

  • Quan GuoEmail author


In the same commercial ecosystem, although the different main bodies of logistics service such as transportation, suppliers and purchasers drive their interests differently, all the different stakeholders in the same business or consumers coexist mutually and share resources with each other. Based on this, this paper constructs a model of bonded logistics supply chain management based on the theory of commercial ecology, focusing on the logistics mode of transportation and multi-attribute behavior decision-making model based on the risk preference of the mode of transport of goods. After the weight is divided, this paper solves the model with ELECTRE-II algorithm and provides a scientific basis for decision-making of bonded logistics supply chain management through the decision model and ELECTRE-II algorithm.


Aviation Cargo transportation mode ELECTRE-II algorithm Behavior analysis 


  1. 1.
    Lupo, T.: Fuzzy ServPerf model combined with ELECTRE III to comparatively evaluate service quality of international airports in Sicily. J Air Transp Manag 42, 249–259 (2015)CrossRefGoogle Scholar
  2. 2.
    Lin, W., Chen, B., Xie, L., et al.: Estimating energy consumption of transport modes in China using DEA. Sustainability 7(4), 4225–4239 (2015)CrossRefGoogle Scholar
  3. 3.
    Banomyong, R., Thai, V.V., Yuen, K.F.: Assessing the national logistics system of Vietnam. Asian J Shipp Logist 31(1), 21–58 (2015)CrossRefGoogle Scholar
  4. 4.
    Choi, J., Yong, S.P., Ju, D.P.: Development of an aggregate air quality index using a PCA-based method: a case study of the US transportation sector. Am J Ind Bus Manag 5(2), 53–65 (2015)Google Scholar
  5. 5.
    Fan, F., Lei, Y.: Decomposition analysis of energy-related carbon emissions from the transportation sector in Beijing. Transp Res Part D Transp Environ 42, 135–145 (2016)CrossRefGoogle Scholar
  6. 6.
    Beifert, A.: Role of air cargo and road feeder services for regional airports—case studies from the Baltic Sea Region. Transp Telecommun J 17(2), 87–99 (2016)CrossRefGoogle Scholar
  7. 7.
    Azadian, F., Murat, A., Chinnam, R.B.: An unpaired pickup and delivery problem with time dependent assignment costs: application in air cargo transportation. Eur J Oper Res 263(1), 188–202 (2017)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Wang, Y., Xu, J., Han, Y., et al.: DeepBurning: automatic generation of FPGA-based learning accelerators for the Neural Network family. In: Proceedings of the 53rd Annual Design Automation Conference, June 05–09, 2016, Austin, TX, USAGoogle Scholar
  9. 9.
    Nam, S.J., Yoo, J., Lee, H.S., et al.: Quantitative evaluation for differentiating malignant and benign thyroid nodules using histogram analysis of grayscale sonograms. J Ultrasound Med 35(4), 775–782 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of ManagementChina University of Mining and TechnologyXuzhouChina
  2. 2.School of BusinessGlobal Institute of Software TechnologySuzhouChina

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