Overview of Logistics Equilibrium Distribution Networks System: An Urban Perspective

  • Wang Wei
  • Md Arafatur RahmanEmail author
  • Md Jahan Ali
  • Md Zakirul Alam Bhuiyan
  • Liu Yao
  • Hai Tao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11342)


Logistics Equilibrium Distribution Networks System is a design scheme which provides the logistics distribution mechanism effective and efficient in terms of several layering aspects: business layout layer, supervision and evaluation layer and planning control layer. It enhances the monitoring function of the information platforms and the design scheme of the planning by controlling the distribution layer moving forward to control the whole system macroscopically to ensure the effective operation. To develop such network toward Urban perspective is a challenging task because of the various distribution layouts control. To address such an issue, this paper proposes a hierarchical ranking urban logistics equilibrium system, which incorporates the functional structure, the distribution system structure, and the operation mechanism in order to realize the high-end and integration of distribution system. The outcome of this research will assist to design an urban distribution system which can improve the distribution efficiency of urban logistics, save transportation costs, reduce carbon emissions, protect the urban environment, and promote the development of urban economy.


Logistics equilibrium Distribution Networks System Economic development Urban logistics 



This work is partially supported by Grants (PGRS170330) and (RDU180341) funded by University Malaysia Pahang.


  1. 1.
    Taniguchi, E., Thompson, R.G. (eds.): City Logistics: Mapping the Future. CRC Press, Boca Raton (2014)Google Scholar
  2. 2.
    Anderson, S., Allen, J., Browne, M.: Urban logistics—how can it meet policy makers’ sustainability objectives? J. Transp. Geogr. 13(1), 71–81 (2005)CrossRefGoogle Scholar
  3. 3.
    Bodnar, T., Okhrin, O., Parolya, N.: Optimal shrinkage estimator for high-dimensional mean vector (2016)Google Scholar
  4. 4.
    Chen, Y., Jiang, Y., Wahab, M.I.M., Long, X.: The facility layout problem in non-rectangular logistics parks with split lines. Expert Syst. Appl. 42(21), 7768–7780 (2015)CrossRefGoogle Scholar
  5. 5.
    Cheng, Z., Zhang, P.: Notice of retraction study on logistics equilibrium early warning control of steel corporation industrial port, vol. 3, July 2010Google Scholar
  6. 6.
    dell’Olio, L., Moura, J.L., Ibeas, A., Cordera, R., Holguin-Veras, J.: Receivers’ willingnessto-adopt novel urban goods distribution practices. Transp. Res. Part A: Policy Pract. 102, 130–141 (2016)Google Scholar
  7. 7.
    Devi, K., Yadav, S.P.: A multicriteria intuitionistic fuzzy group decision making for plant location selection with ELECTRE method. Int. J. Adv. Manuf. Technol. 66, 1–11 (2013)CrossRefGoogle Scholar
  8. 8.
    Engel, T., Sadovskyi, O., Boehm, M., Heininger, R.: A conceptual approach for optimizing distribution logistics using big data (2014)Google Scholar
  9. 9.
    Gonzalez-Feliu, J., Semet, F., Routhier, J.L.: Sustainable Urban Logistics: Concepts, Methods and Information Systems. Springer, Heidelberg (2014). Scholar
  10. 10.
    Gutjahr, W.J., Dzubur, N.: Bi-objective bilevel optimization of distribution center locations considering user equilibria. Transp. Res. Part E: Logist. Transp. Rev. 85, 1–22 (2016)CrossRefGoogle Scholar
  11. 11.
    Hu, J.S., Zhao, G.L.: Supply chain network equilibrium with loss-averse retailers under fuzzy demand. Control Decis. 29, 1899–1906 (2014)Google Scholar
  12. 12.
    Kova´cs, G.: Possible methods of application of electronic freight and warehouse exchanges in solving the city logistics problems. Period. Polytech. Transp. Eng. 38(1), 25 (2010)CrossRefGoogle Scholar
  13. 13.
    Lan, B., Peng, J., Chen, L.: An uncertain programming model for competitive logistics distribution center location problem. Am. J. Oper. Res. 5(6), 536 (2015)CrossRefGoogle Scholar
  14. 14.
    Li, L., Liu, Y.: Nonlinear regression prediction of the social logistics demand forecast in our country. J. Jiangnan Univ. (Nat. Sci. Ed.) 3(13), 375–377 (2014)Google Scholar
  15. 15.
    Liu, W., Ge, M., Yang, D.: An order allocation model in a two-echelon logistics service supply chain based on the rational expectations equilibrium. Int. J. Prod. Res. 51(13), 3963–3976 (2013)CrossRefGoogle Scholar
  16. 16.
    Ma, Y., Yan, F., Kang, K., Wei, X.: A novel integrated production-distribution planning model with conflict and coordination in a supply chain network. Knowl. Based Syst. 105, 119–133 (2016)CrossRefGoogle Scholar
  17. 17.
    Mancini, S., Gonzalez-Feliu, J., Crainic, T.G.: Planning and optimization methods for advanced urban logistics systems at tactical level. In: Gonzalez-Feliu, J., Semet, F., Routhier, J.L. (eds.) Sustainable Urban Logistics: Concepts, Methods and Information Systems. EcoProduction (Environmental Issues in Logistics and Manufacturing). ECOPROD, pp. 145–164. Springer, Heidelberg (2014). Scholar
  18. 18.
    Quak, H.: Sustainability of urban freight transport: retail distribution and local regulations in cities (2008)Google Scholar
  19. 19.
    Rodrigue, J.P., Comtois, C., Slack, B.: The Geography of Transport Systems. Taylor & Franci, Milton Park (2016)CrossRefGoogle Scholar
  20. 20.
    van Schagen Lindawati, J., Goh, M., Souza, R.: Collaboration in urban logistics: motivations and barriers. Int. J. Urban Sci. 18(2), 278–290 (2014)CrossRefGoogle Scholar
  21. 21.
    Sheriff, K.M.M., Nachiappan, S., Min, H.: Combined location and routing problems for designing the quality-dependent and multi-product reverse logistics network. J. Oper. Res. Soc. 65(6), 873–887 (2014)CrossRefGoogle Scholar
  22. 22.
    Wang, G., Gunasekaran, A., Ngai, E.W., Papadopoulos, T.: Big data analytics in logistics and supply chain management: certain investigations for research and applications. Int. J. Prod. Econ. 176, 98–110 (2016)CrossRefGoogle Scholar
  23. 23.
    Yamada, T.: Cooperative freight transport systems. In: City Logistics: Mapping The Future (2014)CrossRefGoogle Scholar
  24. 24.
    Bhuiyan, Z.A., Wang, G., Wang, T., Rahman, A., Wu, J.: Content-centric event-insensitive big data reduction in internet of things. In: 2017 IEEE Global Communications Conference GLOBECOM 2017—Proceedings, vol. 2018, pp. 1–6, January 2018Google Scholar
  25. 25.
    Rahman, M.A., Ali, J., Kabir, M.N., Azad, S.: A performance investigation on IoT enabled intra-vehicular wireless sensor networks. Int. J. Automot. Mech. Eng. 14(1), 3970–3984 (2017)CrossRefGoogle Scholar
  26. 26.
    Wang, T., Bhuiyan, M.Z.A., Wang, G., Rahman, M.A., Wu, J., Cao, J.: Big data reduction for a smart city’s critical infrastructural health monitoring. IEEE Commun. Mag. 56(3), 128–133 (2018)CrossRefGoogle Scholar
  27. 27.
    Bhuiyan, Md.Z.A., Zaman, M., Wang, G., Wang, T., Rahman, Md.A., Tao, H.: Protected bidding against compromised information injection in IoT-based smart grid. In: The 2nd EAI International Conference on Smart Grid and Internet of Things (SGIoT 2018), Niagara Falls, Canada, 11–13 July 2018Google Scholar
  28. 28.
    Rahman, Md.A., Kabir, M.N., Azad, S., Ali, J.: On mitigating hop-to-hop congestion problem in IoT enabled intra-vehicular communication. In: The IEEE International Conference on Software Engineering and Computer Systems, Kuantan, 19–21 August 2015Google Scholar
  29. 29.
    Luo, M.Z.A., Bhuiyan, G., Wang, M.A., Rahman, J., Atiquzzaman, M.: PrivacyProtector: privacy-protected patient data collection in IoT-based healthcare systems. IEEE Commun. Mag. 56(2), 163–168 (2018)CrossRefGoogle Scholar
  30. 30.
    Abbas, A.M., Ali, J., Rahman, M.A., Azad, S.: Comparative investigation on CSMA/CA-based MAC protocols for scalable networks. In: 2016 International Conference on Computer and Communication Engineering (ICCCE), pp. 428–433. IEEE (2016)Google Scholar
  31. 31.
    Rahman, M.A., Mezhuyev, V., Bhuiyan, M.Z.A., Sadat, S.N., Zakaria, S.A.B., Refat, N.: Reliable decision making of accepting friend request on online social networks. IEEE Access 6, 9484–9491 (2018)CrossRefGoogle Scholar
  32. 32.
    Kabir, M.N., Rahman, M.A., Azad, S., Azim, M.M.A., Bhuiyan, M.Z.A.: A connection probability model for communications networks under regional failures. Int. J. Crit. Infrastruct. Prot. 20, 16–25 (2018)CrossRefGoogle Scholar
  33. 33.
    Abbas, A., Rahman, M.A., Kabir, M.N., Zamli, K.Z.B.: Scalable MAC strategy for emergency communication networks. Adv. Sci. Lett. 24(10), 7407–7417 (2018)CrossRefGoogle Scholar
  34. 34.
    Rahman, M.A., Asyhari, A.T., Bhuiyan, M.Z.A., Salih, Q.M., Zamli, K.Z.B.: L-CAQ: joint link-oriented channel-availability and channel-quality based channel selection for mobile cognitive radio networks. J. Netw. Comput. Appl. 113, 26–35 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Wang Wei
    • 1
  • Md Arafatur Rahman
    • 2
    • 4
    Email author
  • Md Jahan Ali
    • 2
  • Md Zakirul Alam Bhuiyan
    • 3
  • Liu Yao
    • 1
  • Hai Tao
    • 5
  1. 1.Faculty of Industrial ManagementUniversity Malaysia PahangGambangMalaysia
  2. 2.Faculty of Computer Systems and Software EngineeringUniversity Malaysia PahangGambangMalaysia
  3. 3.Department of Computer and Information SciencesFordham UniversityNew YorkUSA
  4. 4.IBM, Center of Excellence, UMPGambangMalaysia
  5. 5.School of Computer ScienceBaoji University of Art and ScienceBaojiChina

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