Cluster Computing

, Volume 22, Supplement 6, pp 15335–15345 | Cite as

Effect of carbon emission policy on fresh aquatic product carriers in augmented reality respective: a parallel simulation research based on genetic algorithm

  • Minjing PengEmail author
  • Fei Yang


During the transportation of aquatic products, living states of the aquatic products and carbon emission of the carriers are respectively the concerns of the carrier enterprises and the government. Drivers adds virtual living states of aquatic products to the real driving environments through augmented reality technology to help themselves control oxygen contents and temperatures, and choose distribution routes. Furthermore, the effectiveness of carbon emission policy is investigated by simulating the impact of the policy on the distribution operations. In the simulation, the carriers take the minimization of overall costs of the distribution operations as the objective. In order to meet the requirements of carbon emission policies, factors of customer demands, road lengths and road congestion are used to determine the distribution. The simulation is implemented based on genetic algorithm: total costs are used as fitness values, and distribution sequences are encoded into chromosomes. And the carbon emission price is introduced as a operating parameter. Through running the simulation, we obtained the overall costs, carbon emission amounts and carbon emission costs. And a linear equation are fitted on the output data passed the reliability test. Based on the linear equation, it is concluded that the increase of the carbon emission price is helpful in reducing carbon emission amount, but it would greatly increase the operation costs of the carriers.


Carbon emission policy Aquatic product Genetic algorithm Augmented reality Carbon emission price 



This work is supported by National Science Foundation of China (Grant: 71203162), Science and Technology Planning Project of Guangdong Province (Grant: 2014B040404072), Natural Science Foundation of Guangdong Province (Grant: 2015A030313642), Innovation Project of Wuyi University (Grant: 2014KTSCX128 and 2015KTSCX144).


  1. 1.
    Zhang, G., Habenicht, W., Ernst, L.S.W.: Improving the structure of deep frozen and chilled food chain with tabu search procedure. J. Food Eng. 60(1), 67–79 (2013)CrossRefGoogle Scholar
  2. 2.
    Montanari, R.: Cold chain tracking: a managerial perspective. Trends Food Sci. Technol. 19(8), 425–431 (2008)CrossRefGoogle Scholar
  3. 3.
    Arbelaitz, O., Rodriguez, C.: Comparison of systems based on evolutionary search and simulated annealing to solve the VRPTW problem. Int. J. Comput. Intell. Appl. 4(1), 27–39 (2004)zbMATHCrossRefGoogle Scholar
  4. 4.
    Ho, W., Ang, J.C., Lim, A.: A hybrid search algorithm for the vehicle routing problem with time windows. Int. J. Artif. Intell. Tools 10(3), 431–449 (2001)CrossRefGoogle Scholar
  5. 5.
    Osvalda, A., Stirn, L.Z.: A vehicle routing algorithm for the distribution of fresh vegetables and similar perishable food. J. Food Eng. 85(2), 285–295 (2008)CrossRefGoogle Scholar
  6. 6.
    Wu, Z.: Optimization of distribution route selection based on particle swarm algorithm. Int. J. Simul. Model. 13(2), 230–242 (2014)CrossRefGoogle Scholar
  7. 7.
    Dhakal, S.: Urban energy use and carbon emissions from cities in China and policy implications. Energy Policy 37(11), 4208–4219 (2009)CrossRefGoogle Scholar
  8. 8.
    Cardona-Valdés, Y., Álvarez, A., Ozdemir, D.: A bi-objective supply chain design problem with uncertainty. Transp. Res. C 19(5), 821–832 (2011)CrossRefGoogle Scholar
  9. 9.
    Su, M., Li, R., Lu, W.: Evaluation of a low-carbon city: method and application. Entropy 15(4), 1171–1185 (2013)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Bloemhof-Ruwaard, J., Dekker, R., Fleischmann, M., et al.: Quantitative models for reverse logistics: a review. Eur. J. Oper. Res. 103(1), 1–17 (1997)zbMATHCrossRefGoogle Scholar
  11. 11.
    Hoen, K.M.R., Tan, T., Fransoo, J.C., van Houtum, G.J.: Effect of carbon emission regulations on transport mode selection under stochastic demand. Flex. Serv. Manuf. J. 26, 170–195 (2014)CrossRefGoogle Scholar
  12. 12.
    Zhang, L., Wang, Y., Fei, T., Ren, H.: The research on low carbon logistics routing optimization based on DNA-ant colony algorithm. Discrete Dyn. Nat. Soc. 10, 1–13 (2014)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Benjaafar, S., Li, Y., Daskin, M.: Carbon footprint and the management of supply chains: insights from simple models. IEEE Trans. Autom. Sci. Eng. 10(1), 99–116 (2013)CrossRefGoogle Scholar
  14. 14.
    Hu, H., Li, Y.: Research on routing optimization of regional logistics based on gravity model: a case of blue and yellow zones. iBusiness 5(4), 167–172 (2013)CrossRefGoogle Scholar
  15. 15.
    Hummels, D.: Transport costs and international trade in the second era of globalization. J. Econ. Persp. 21(3), 131–154 (2007)CrossRefGoogle Scholar
  16. 16.
    Gonzales, D., Searcy, E.M., Eksiku, S.D.: Cost analysis for high-volume and long-haul transportation of densified biomass feedstock. Transp. Res. Part A 49, 48–61 (2013)CrossRefGoogle Scholar
  17. 17.
    Tseng, Y., Yue, W.L., Taylor, M.: The role of transportation in logistic chain. Proc. Eastern Asia Soc. Transp. Stud. 5, 1657–1672 (2005)Google Scholar
  18. 18.
    Lu, C., Tong, Q., Liu, X.: The impacts of carbon tax and complementary policies on Chinese economy. Energy Policy 38(11), 7278–7285 (2010)CrossRefGoogle Scholar
  19. 19.
    Grema, L.U., Abubakar, A.B., Obiukwu, O.O.: Carbon emission control measures. Int. Lett. Nat. Sci. 3, 21–27 (2013)Google Scholar
  20. 20.
    Holland, J.H.: Studying complex adaptive systems. J. Syst. Sci. Complexity 19(1), 1–8 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Liu, Z., Sun, Z.: The carbon trading price and trading volume forecast in Shanghai city by BP neural network. World Acad. Sci. Eng. Technol. 11(3), 623–629 (2017)Google Scholar

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

Authors and Affiliations

  1. 1.School of Economics and ManagementWuyi UniversityJiangmenChina
  2. 2.Engineering Technology Research Center for E-Commerce Augmented Reality of Guangdong ProvinceJiangmenChina

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