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.
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.
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.
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