Top > JACIII > Selection of Logistic Web Services Based on Fuzzy Evaluati ...

single-jc.php

JACIII Vol.18 No.5 pp. 798-804
doi: 10.20965/jaciii.2014.p0798
(2014)

Paper:

Views over last 60 days: 883

Selection of Logistic Web Services Based on Fuzzy Evaluation on Principal Component of Quality of Service

Dongmin Li*,** and Huanshui Zhang*

*School of Control Science and Engineering, Shandong University, No.17923, JingShi Street, Ji’nan, Shandong 250061, China

**Department of Mechanical and Electronic Engineering, Shandong University of Science and Technology, No.223, DaiZong Street, Tai’an, Shandong 271019, China

Received:
October 29, 2013
Accepted:
May 15, 2014
Published:
September 20, 2014
Creative Commons License
Keywords:
quality of logistic web service, service selection, principal components analysis, comprehensive evaluation system, trapezoidal fuzzy number
Abstract
The current results on logistic Web services selection are not optimal due to some key quality indexes of logistic Web services excluded, in order to resolve the above problem, an evaluation system on quality of service is established by use of principal component analysis based on quality of logistic service, quality of Web service, and satisfaction of customers. The values of quality of service with subjective uncertainty in the evaluation system are given with trapezoidal fuzzy number according to the definition of logistic business and evaluation from domain experts and customers, besides, the weight on each quality of service is given by pairwise comparison, and an algorithm based on analytic hierarchy process for logistic Web service selection is established. The optimal service is got by adopting the algorithm in the logistic scenario on automotive transportation, which proves that the way on service selection in this paper is feasible and effective.
Cite this article as:
D. Li and H. Zhang, “Selection of Logistic Web Services Based on Fuzzy Evaluation on Principal Component of Quality of Service,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.5, pp. 798-804, 2014.
Data files:
References
  1. [1] D. Aloini, R. Dulmin, and V. Mininno, “A hybrid Fuzzy-Promethee method for logistic service selection–Design of a decision support tool,” 9th Int. Conf. on Intelligent Systems Design and Applications, pp. 462-466, 2009.
  2. [2] A. Parasuraman, V. A. Zeithanml, and L. L. Berry, “A Conceptual Model of Service Quality and Its Implication For Future Research,” J. of Marketing, Vol.49, No.4, pp. 41-50, 1985.
  3. [3] T. Yu and K.-J. Lin, “Service Selection Algorithms for Composing Complex Services with Multiple QoS Constraints,” 3rd Int. Conference on Service Oriented Computing, pp. 130-143, 2005.
  4. [4] T. Yu, Y. Zhang, and K.-J. Lin, “Efficient algorithms for Web services selection with end-to-end QoS constraints,” ACM Transactions on Web, Vol.1, No.1, pp. 6-32, 2007.
  5. [5] S. Meixler and U. Brinkschulte, “A selection method for services in dynamic environments,” 14th IEEE Int. Symp. on Object/Component/Service-Oriented Real-Time Distributed Computing, pp. 19-23, 2011.
  6. [6] A. Danilo and P. Barbara, “Adaptive service composition in flexible processes,” IEEE Trans. on Software Engineering, Vol.33, No.6, pp. 369-384, 2007.
  7. [7] Y. Anis, G. Ole-Christoffer, and O. B. John, “Service selection in stochastic environments: A learning-automaton based solution,” Applied Intelligence, Vol.36, No.3, pp. 617-637, 2012.
  8. [8] Z. Chengwen, S. Sen, and C. Junliang, “Genetic Algorithm on Web Services Selection Supporting QoS,” Chinese J. of Computers, Vol.29, No.7, pp. 1029-1037, 2006.
  9. [9] G. Canfora, M. Dipenta, and R. Esposito, “An approach for QoSaware service composition based on genetic algorithms,” Proc.s of the 2005 Conf. on Genetic and Evolutionary Computation, pp. 1069-1075, 2005.
  10. [10] R. P. Singh and K. K. Pattanaik, “An Approach to Composite QoS Parameter based Web Service Selection,” Procedia Computer Science, Vol.19, pp. 470-477, 2013.
  11. [11] S. Qin, Y. Chen, and X. Mu, “An Optimal Service Selection with Constraints Based on QoS,” Physics Procedia, Vol.25, pp. 2050-2057, 2012.
  12. [12] L. Zhang, C. Li, and Z. Yu, “DynamicWeb Service Selection Group Decision-making Based on Heterogeneous QoS Models,” The J. of China Universities of Posts and Telecommunications, Vol.19, No.3, pp. 80-90, 2012.
  13. [13] C. Sijia, “Research and Application of Logistics Service Quality Evaluation,” Beijing Jiaotong University, China, 2012.
  14. [14] E. Holschbach, W. Stölzle, and T. Friedli, “Quality Management Practices for Business Services from a Buyer’s Perspective,” University of St.Gallen, Switzerland, 2011.
  15. [15] L. Yibin, D. Qianli, and S. Haojie, “Review of Quality Management and Measurement for Logistics Services,” Logistic Technology, Vol.31, No.4, pp. 14-17, 2012.
  16. [16] W. Wenling and C. Dapeng, “Application of Principle Component Analysis in Choosing the Location of Logistics Zone,” Logistic Sci-Tech, No.9, pp. 35-37,67, 2009.
  17. [17] Z. Qilan, “Research on Mass Customization Logistics Service Capability,” Beijing Jiaotong University, China, 2010.
  18. [18] Y. Liu, A. H. H. Ngu, and L. Zeng, “QoS Computation and Policing in Dynamic Web Service Selection,” Proc. of the 13th Int. Conf. on World Wide Web, pp. 66-73, 2004.
  19. [19] W. Shangguang, S. Qibo, and Y. Fangchun, “Web Service Dynamic Selection by the Decomposition of Global QoS Constraints,” J. of Software, Vol.22, No.7, pp. 1426-1439, 2011.
  20. [20] I. Meidutė-Kavaliauskienė, A. Aranskis, and M. MLitvinenko, “Consumer Satisfaction with the Quality of Logistics Services,” Procedia-Social and Behavioral Sciences, Vol.110, pp. 330-340, 2014.
  21. [21] J. Peng, “Selection of Logistics Outsourcing Service Suppliers Based on AHP,” Energy Procedia, Vol.17, pp. 595-601, 2012.
  22. [22] H. Biqing,W. Ting, and X. Xiao, “Service-selecting approach based on domain-specified QoS model and its application in logistics,” Service Industries J., Vol.32, No.9, pp. 1571-1588, 2012.
  23. [23] K. C. Lam, R. Tao, and M. C. K. Lam, “A Material Supplier Selection Model for Property Developers Using Fuzzy Principal Component Analysis,” Automation in Construction, Vol.19, No.5, pp. 608-618, 2010.
  24. [24] L. Cao and H.Wen, Application of Mathematical Statistics,” Harbin Institute of Technology Press, China, 2012.
  25. [25] K. Lee, J. Jeon,W. Lee, S. Jeong, et al., “QoS for Web Services: Requirementsand Possible Approaches,” W3C Working Group Note 25 Nov. 2003,
    http://www.w3c.or.kr/kr-office/TR/2003/ws-qos/ [Accessed June 10, 2013]
  26. [26] C. Xiaohong, H. Wen-hua, C. Yu, et al., “Hierarchical Multiple Objective Programming Model for Multiple Attribute Decision Making Problems Based on Trapezoidal Fuzzy Numbers,” J. of Industrial Engineering/Engineering Management, Vol.26, No.4, pp. 192-198, 2012.
  27. [27] W.Ho, T. He, C.K.M. Lee et al., “Strategic Logistics Outsourcing: An Integrated QFD and Fuzzy AHP Approach,” Expert Systems with Applications, Vol.39, No.12, pp. 10841-10850, 2012.
  28. [28] A. Awasthi and S. S. Chauhan, “A Hybrid Approach Integrating Affinity Diagram, AHP and Fuzzy TOPSIS for Sustainable City Logistics Planning,” Applied Mathematical Modelling, Vol.36, No.2, pp. 573-584, 2012.
  29. [29] Y. Li, X. Liu, and Y. Chen, “Selection of Logistics Center Location Using Axiomatic Fuzzy Set and TOPSIS Methodology in LogisticsManagement,” Expert Systems with Applications, Vol.38, No.6, pp. 7901-7908, 2011.
  30. [30] D. Li, B. Huang, and P. Zhong, et al., “Framework of Networked Logistic Service Matching Based on SAWSDL,” Journal of Huaqiao University (Natural Science), Vol.31, No.3, pp. 275-281, 2010.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Oct. 01, 2024