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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.