Design and development of an intelligent context-aware decision support system for real-time monitoring of container terminal operations

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30 Citations (Scopus)

Abstract

This paper describes a case study of the research and development of an intelligent context-aware decision support system (ICADSS) prototype for real-time monitoring of container terminal operations in Hong Kong. We present the system design and development of the prototype system, and discuss the experiences and lessons learned. To the best of our knowledge, this study is the first identifiable application of an intelligent context-aware decision support system for the real-time monitoring of container terminal operations reported in the academic literature. The intelligent context-aware decision support system employs ZigBee-based ubiquitous sensor network (USN) technology. In this study, an ICADSS prototype was built and implemented in a real world setting. The results of the system prototype evaluation are satisfactory and support the contention that it is more effective in supporting the real-time tracking and tracing of container trucks, quay cranes, and rubber-tired gantry cranes in a container terminal. The results also validate the practical viability of the proposed system architecture. Given the contextual details of the study, we present the lessons learned from developing and operating the system in a container terminal and provide suggestions for further research. We hope that the proposed system architecture and developed prototype system can help both practitioners and academics in the further use and research of intelligent context-aware decision support systems.
Original languageEnglish
Pages (from-to)3501-3526
Number of pages26
JournalInternational Journal of Production Research
Volume49
Issue number12
DOIs
Publication statusPublished - 15 Jun 2011

Keywords

  • container terminal
  • operations monitoring
  • RFID technology
  • ubiquitous sensor network
  • ZigBee

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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