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Neural Network Model for the Risk Prediction in Cold Chain Logistics

첫 페이지 보기
  • 발행기관
    보안공학연구지원센터(IJMUE) 바로가기
  • 간행물
    International Journal of Multimedia and Ubiquitous Engineering SCOPUS 바로가기
  • 통권
    Vol.9 No.8 (2014.08)바로가기
  • 페이지
    pp.111-124
  • 저자
    Weiyang Xu, Zhenji Zhang, Daqing Gong, Xiaolan Guan
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A230554

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원문정보

초록

영어
This study investigates environment sensitive and perishable products (ESPPs) logistics problem, which is called cold chain logistics problem (CCLs). Based on a comprehensive literature review, we found that there is much room to improve regarding of the risks management in cold chain logistics, that is, the development of a comprehensive cold chain logistics design methodology should considered uncertainty sources and risk exposures. In this study, we propose a neural network model to illustrate the problems. Firstly, the paper develops input indicators at different points in cold chain logistics to examine the effects of environment fluctuations including temperature control, humidity monitoring, the temperature interruption time and electric vehicle mapping, etc; secondly, the improved neural network algorithm can achieve model convergence, including the increase of momentum term, the adjustment of learning rate and the change of error function. At last, through simulation, this study shows that comprehensive risk prediction of cold chain logistics will be calculated based on the input indicators using the improved neural network algorithm, and the predictive value is accurate. So not only the analyzing of kinds of cold chain logistics indicators can be realized through the Neural Network model, but we can take priorities resorting to the predictive results accordingly.

목차

Abstract
 1. Introduction
 2. Literature Review
 3. The Neural Network Predictive Model
 3.1. The Standard Neural Network Model
 3.2. The Neural Network Algorithm
 4. The Improved BP Neural Network Algorithm
  4.1. Increasing Momentum Term and the Adjustment of Learning Rate [27]
  4.2. Transforming the Input Data and the Design of Hidden Layer
  4.3. Changing the Error Function and Transformation Function
 5. Neural Network-based Simulation on Risk Prediction in CCLM
  5.1. Simulation Background
  5.2. Network Learning and Testing
 6. Conclusions
 Acknowledgement
 References

키워드

environment sensitive and perishable products (ESPPs) cold chain logistics (CCLs) input indicators electric vehicle Neural Network

저자

  • Weiyang Xu [ School of Economics and Management, Beijing Jiaotong University, China ]
  • Zhenji Zhang [ School of Economics and Management, Beijing Jiaotong University, China ]
  • Daqing Gong [ School of Economics and Management, Beijing Jiaotong University, China ] Corresponding author
  • Xiaolan Guan [ School of Economics and Management, Beijing Institute of Graphic Communication, China ]

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    보안공학연구지원센터(IJMUE) [Science & Engineering Research Support Center, Republic of Korea(IJMUE)]
  • 설립연도
    2006
  • 분야
    공학>컴퓨터학
  • 소개
    1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업

간행물

  • 간행물명
    International Journal of Multimedia and Ubiquitous Engineering
  • 간기
    월간
  • pISSN
    1975-0080
  • 수록기간
    2008~2016
  • 등재여부
    SCOPUS
  • 십진분류
    KDC 505 DDC 605

이 권호 내 다른 논문 / International Journal of Multimedia and Ubiquitous Engineering Vol.9 No.8

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