Saturday , June 23 2018

A Holographic-based Model for Logistics Resources Integration

Daqing GONG, Shifeng LIU
School of Economics & Management,
Beijing Jiaotong University, China;

Abstract: This study investigates the logistics resource integration problem. Based on a comprehensive literature review, we find that there is much room for improvement regarding the robustness problems in logistics resources integration. Logistics resources integration should especially consider uncertainties. In this study, we propose a holographic-based model (Internet of Things and Neural Network) to illustrate the problem. Internet of Things (IoT) is able to receive real-time data (including uncertainty information) in logistics systems and is equivalent to the perception subsystem. Neural Network, on the other hand, can determine the overall operation state for logistics resources integration and plays the role of analysis and assessment. Through simulation, this study shows that real-time data in logistics systems are transmitted based on protocols, so that uncertainty information can be received by the IoT model. The Neural Network model can comprehensively evaluate uncertainties through the neural network algorithm. Therefore, the robustness of logistics resources integration can be ensured in the logistics system.

Keywords: logistics resources integration, holographic, Internet of Things (IoT), Neural Network.

<Full text
CITE THIS PAPER AS: Daqing GONG, Shifeng LIU, A Holographic-based Model for Logistics Resources Integration, Studies in Informatics and Control, ISSN 1220-1766, vol. 22 (4), pp. 367-376, 2013.

  1. Introduction

Logistics resource usually refers to various logistics infrastructure. However, it also includes an organization’s production factors like capital, technologies, knowledge, information, personnel, equipment, customer, organization, culture, etc. in logistics services and working processes. According to Xu (2005), all of the economic systems, social systems, and natural systems are logistics resources in the view of material flow. With the development of logistics industry, logistics system will become more and more complex. Under these circumstances, the problems of logistics resources integration need to be solved urgently.

Based on the findings from existing literatures, we found that the existing studies discuss logistics system resources in natural languages or focus on the application of mathematics and information technologies in logistics. However, the quantitative description of complex logistics resources cannot be substituted by natural languages, mathematics or information technologies. In this study, we focus on the ‘robustness’, which refers to the rapid acquisition, learning, feeding back, and intelligent analysis towards a variety of uncertainties information. According to Klibi et al. (2010), the development of a comprehensive SCN (Supply Chain Network) design methodology should consider uncertainty sources and risk exposures. Therefore Manuj and Mentzer (2008) discussed that high-impact extreme events should not be treated in the same way as low-impact business-as-usual events. Lempert et al. (2006) discussed that it is possible to elaborate plausible future scenarios under deep uncertainties. Similarly, Snyder (2006) defined robustness as the extent in which a SCN is able to carry its functions for a variety of plausible future scenarios. The question remains on how we can tackle this problem. Tomlin (2006) suggested flexible sourcing contracts as a hedge against randomness and hazards, in order to increase the SCN expected value. Sheffi (2005) used the physical insurance against SCN’s risk exposure to circumvent disruptions as much as possible, as well as to bounce back quickly when struck. So the research questions in this study are: how to realize the quantitative descriptions of the complex logistics resources and how to make the SCN robust. At present, the aforementioned problems have not been resolved yet in related research works.

In this study, we propose a holographic-based model to illustrate the problems of logistics resource integration. We organize this paper as follows: in the following section, we present a comprehensive review, which forms the theoretical foundation of this study. In section 3, we present the analytical models as well as the simulation results through which we verify the model. Finally, we conclude the study in section 4.


  1. KLIBI, W., A. MARTEL, A. GUITOUNI, The Design of Robust Value-Creating Supply Chain Networks: A Critical Review, Eur. J. of Op. Res., vol. 203(2), 2010, pp. 283-293.
  2. MANUJ, I., J. MENTZER, Global Supply Chain Risk Management, J. of Bus. Log., vol. 29(1), 2008, pp. 133-155.
  3. LEMPERT, R., D. GROVES, S. POPPER, S. BANKES, A General, Analytic Method for Generating Robust Strategies and Narrative Scenarios, Man. Sc., vol. 52(4), 2006, pp. 514-528.
  1. SNYDER, L., Facility Location under Uncertainty: A Review, IIE Trans., vol. 38(7), 2006, pp. 537-554.
  2. TOMLIN, B., On the Value of Mitigation and Contingency Strategies for Managing Supply Chain Disruption Risks, Man. Sc., vol. 52(5), 2006, pp. 639-657.
  3. SHEFFI, Y., The Resilient Enterprise: Overcoming Vulnerability for Competitive Advantage, MIT Press Books, 2005.
  4. LEE, H., Creating Value through Supply Chain Integration,, 2000.
  5. LEPAK, D., K. SMITH, M. TAYLOR, Value Creation and Value Capture: A Multilevel Perspective, Ac. Man. Rev., vol. 32(1), 2007, pp. 180-194.
  6. BRINTRUP, A., Behaviour Adaptation in The Multi-agent, Multi-objective and Multi-role Supply Chain, Computers in Industry, vol. 61(7), 2010, pp. 636-645.
  7. AKANLE, O., D. ZHANG, Agent-based Model for Optimising Supply-Chain Configurations, Intl. J. of Prod. Ec., vol. 115(2), 2008, pp. 444-460.
  8. IAO, J., X. YOU, A. KUMAR, An Agent-based Framework for Collaborative Negotiation in The Global Manufacturing Supply Chain Network, Rob. & Comp.-Int. Manuf., vol. 22(3), 2006, pp. 239-255.
  9. PLOOG, M., T. SPENGLER, Integrated Planning of Electronic Scrap Disassembly and Bulk Recycling, IEEE Intl. Sym. on El. and Environ., 2002.
  10. TSERNG, H., S. YIN, S. LI, Developing A Resource Supply Chain Planning System for Construction Projects, J. Constr. Eng. & Man., vol. 132(4), 2006, pp. 393-407.
  11. BHATTACHARYYA, K., Value Sourcing in Supply Chains, Unpublished PhD diss., Kent State University, Kent, 2011.
  12. DYSON, B., N. CHANG, Forecasting Municipal Solid Waste Generation in A Fast-Growing Urban Region with System Dynamics Modelling, Waste Man., vol. 25(7), 2005, pp. 669-679.
  13. ZANJANI, A., M. SAEEDI, B. KIANI, A. VOSOOGH, The Effect of The Waste Separation Policy in Municipal Solid Waste Management Using The System Dynamic Approach, Intl. J. of Envir. Health Eng., vol. 1(1), 2012, pp. 1-5.
  14. GEORGIADIS, P., M. BESIOU, Environmental and Economical Sustainability of WEEE Closed-loop Supply Chains with Recycling: A System Dynamics Analysis, The Intl. J. of Adv. Man. Tech., vol. 47(5-8), 2010, pp. 475-493.
  15. SCHIERITZ, N., A. Größler, Emergent Structures in Supply Chains-A Study Integrating Agent-Based and System Dynamics Modelling, Proc. 36th Ann. Hawaii Intl. Conf. on Sys. Sc., 2003.
  16. SODHI, M., B. REIMER, Models for Recycling Electronics End-of-life Products, OR Spectrum, vol. 23(1), 2001, pp. 97-115.
  17. PRAJOGO, D., J. OLHAGER, Supply Chain Integration and Performance: The Effects of Long-term Relationships, Information Technology and Sharing, and Logistics Integration, Intl. J. of Prod. Ec., vol. 135(1), 2012, pp. 514-522.
  18. BARTON, R., A. THOMAS, Implementation of Intelligent Systems, Enabling Integration of SMEs to High-Value Supply Chain Networks, Eng. App. of AI, vol. 22(6), 2009, pp. 929-938.
  19. WAMBA, S., A. CHATFIELD, A Contingency Model for Creating Value from RFID Supply Chain Network Projects in Logistics and Manufacturing Environments, Eur. J. of Inf. Sys., vol. 18(6), 2009, pp. 615-636.
  20. HANDFIELD, R., E. NICHOLS, Future Challenges in Supply Chain Management, Introduction to Supply Chain Management, Englewood Cliffs, New Jersey: Prentice-hall, 1999.
  21. SAHAY, B., Supply Chain Collaboration: The Key to Value Creation, Work Study, vol. 52(2), 2003, pp. 76-83.
  22. NAGARAJAN, M., G. SOŠIĆ, G., Game-Theoretic Analysis of Cooperation Among Supply Chain Agents: Review and Extensions, Eur. J. of Op. Res., vol. 187(3), 2008, pp. 719-745.
  23. DZITAC, I., B. E. BARBAT, Artificial Intelligence + Distributed Systems = Agents, Intl. J. of Comp., Comm. & Ctrl, vol. 4(1), 2009 pp. 17-26.
  24. VANOOYEN, A., B. NIENHUIS, Improving The Convergence of The Back-propagation Algorithm, Neural Networks, vol. 53(3), 1992, pp. 465-471.
  25. KITANO, H., Designing Neural Networks Using Genetic Algorithms with Graph Generation System, Complex Sys., no. 4, 1990, pp. 461-476.
  26. MARTIN, T., B. HOWARD, H. MARK, Neural Network Design, Pws Pub, 1996.
  27. HAYKIN, S., Neural Networks and Learning Machines, Pearson Academic, 2008.
  28. FILIP, F. G, Trends and Tools in Systems Analysis, Modelling, Simulation and Control, Systems, Man and Cyb., Proc. Intl. Conf. ‘Syst. Eng. in the Service of Humans’, vol. 4, 1993, pp. 413-416.