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A Combinatorial Assessment Method for the Sequencing Problem and its Application in Waterway Traffic Environments

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Abstract

A combinatorial assessment method (CAM) model is presented to better assess and solve sequencing problems with respect to safety issues in waterway traffic environments. The CAM model proposed involves four parts: the attributive matching model (AMM) and its algorithm, the multi-method calculation, the information filtering model (IFM), and the information fusion algorithm (IFA). Among them, the AMM and its algorithm for the matching level can initially select suitable methods. The multi-method calculation can apply the chosen methods and unify the dimensions of all solutions. The IFM, based on the iterative cluster analysis, can remove errors and interferences through several iterations to improve the solution. The IFA, based on the mean value method, the Borda count rule, and the Copeland method, can examine the convergence of the solutions and provide a high quality one. Another noticeable function is that a customized model for a particular object might be obtainable based on the proposed CAM model. In the second part of the paper, the CAM model is used to solve a channel sequencing problem with respect to the safety levels of a navigable environment of the Yangtze River. A customized CAM-CSP model is formed, whose generality is tested subsequently through a similar but more complicated channel sequencing problem. The solution indicates that the proposed CAM model can enhance the assessment’s quality and help to provide a customized model to solve similar problems conveniently.

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References

  1. Kontovas, C. A. (2005). Formal safety assessment critical review and future role (pp. 1–80). Greece: National Technical University of Athens.

    Google Scholar 

  2. Jiang, Y. C., Nan, Z. R., & Yang, S. C. (2013). Risk assessment of water quality using Monte Carlo simulation and artificial neural network method. Journal of Environmental Management, 122, 130–136.

    Article  CAS  Google Scholar 

  3. Barati, R., & Setayeshi, S. (2014). Probabilistic safety assessment of Tehran research reactor based on a synergy between plant topology and hierarchical evolutions. Progress in Nuclear Energy, 70, 199–208.

    Article  Google Scholar 

  4. Gu, J. F. (1990). Review on assessment methods. Scientific decision-making and systems engineering—the Sixth Conference Proceedings of Systems Engineering Society of China, 22–26.

  5. Lichtenstein, S. (1996). Factors in the selection of a risk assessment method. Computers & Security, 15(5), 401–407.

    Google Scholar 

  6. Chen, G. H., Chen, Y. T., & Li, M. J. (2003). Research on the combination evaluation system. Journal of Fudan University (Natural Science), 42(5), 667–672.

    Google Scholar 

  7. Chen, Y. T., Chen, G. H., & Li, M. J. (2004). Classification & research advancement of comprehensive evaluation methods. Journal of Management Sciences in China, 7(2), 69–79.

    Google Scholar 

  8. Tong, Y. H., Tan, Y., & Rao, Z. H. (2004). Study on the technology assessment methods matching based on contents and objects. Science of Science and Management of S&T, 7, 63–67.

    Google Scholar 

  9. Xu, M. Q., Tan, Y., & Tong, Y. H. (2005). Study on the technology assessment methods matching based on analysis of attributes. Chinese Journal of Management Science, 13(1).

  10. Wang, H. Z., Tong, Y. H., & Xu, M. Q. (2006). Methods system and methods choice about multi-dimensional integrated technology assessment based on public policy-making. Science of Science and Management of S&T, 8, 5–11.

    Google Scholar 

  11. Han, Y., & Tang, X. W. (1999). On choice of an optimum synthetic evaluation method for targets with indicatrixes distributed in certain pattern. Journal of University of Electronic Science and Technology of China, 28(3), 55–58.

    Google Scholar 

  12. Chen, G. H., Li, M. J., & Chen, Y. T. (2004). The research on measurement of drift in the evaluation conclusion of single method. Engineering Science, 6(3), 58–63.

    Google Scholar 

  13. Bates, J. M., & Granger, C. W. J. (1969). The combination of forecasts. Operational Research Quarterly, 20, 451–468.

    Article  Google Scholar 

  14. Greer, M. R. (2005). Combination forecasting for directional accuracy: an application to survey interest rate forecasts. Journal of Applied Statistics, 32(6), 607–615.

    Article  Google Scholar 

  15. Costantini, M., & Pappalardo, C. (2010). Hierarchical procedure for the combination of forecasts. International Journal of Forecasting, 26, 725–743.

    Article  Google Scholar 

  16. Shen, S., Li, G., & Song, H. (2011). Combination forecasts of international tourism demand. Annals of Tourism Research, 38(1), 72–89.

    Article  Google Scholar 

  17. Cang, S., & Yu, H. N. (2014). A combination selection algorithm on forecasting. European Journal of Operational Research, 234, 127–139.

    Article  Google Scholar 

  18. Guo, X. G. (1995). A new synthesized method—combinatorial evaluation method. Statistical Research, 6(5), 56–59.

    Google Scholar 

  19. Chen, G. H., & Li, M. J. (2005). The simulation experiment on verifying the convergence of combination evaluation. Systems Engineering–Theory & Practice, 5, 74–82.

    Google Scholar 

  20. Chen, W. J., Hao, Y. G., Qin, T. R., Li, F., Wang, K. Y. (2005). Combined assessment model of harbor navigation environment safety assessment. Journal of Traffic and Transportation Engineering, (1), 75–77 & 110.

  21. Medda, F., & Nijkamp, P. (2003). A combinatorial assessment methodology for complex transport policy analysis. Complex Transport Policy Analysis, 2003(4), 214–222.

    Google Scholar 

  22. Borda, J. C. (1781). Mémoire sur les élections au scrutin. Paris: Histoire de l'Academie Royal des Sciences.

    Google Scholar 

  23. Abramson, P. R., Aldrich, J. H., Diskin, A., Houck, A. M., Levine, R., & Scotto, T. J. (2013). The British general election of 2010 under different voting rules. Electoral Studies, 32(1), 134–139.

    Article  Google Scholar 

  24. Copeland, A. H. (1951). A reasonable social welfare function. Seminar on application of mathematics to social sciences, University of Michigan.

  25. Lamboray, C. (2007). A comparison between the prudent order and the ranking obtained with Borda’s, Copeland’s, Slater’s and Kemeny’s rules. Mathematical Social Sciences, 54(1), 1–16.

    Article  Google Scholar 

  26. Zhang, T. B., & Chen A. P. (2003). Ships routing system in Jiangsu section of the Yangtze River. Dalian Maritime University Press.

  27. Zhang, Y. C., Cao, C., & Lv, Y. Q. (2005). The chart for ships routing system in Jiangsu section of the Yangtze River. Changjiang Waterway Bureau.

  28. Yang, C. Y., & Cai, W. (2013). Extenics—theory method and application. Beijing: Science Press.

    Google Scholar 

  29. He, Y. X., Dai, A. Y., Zhu, J., He, H. Y., & Li, F. R. (2011). Risk assessment of urban network planning in China based on the matter-element model and extension analysis. International Journal of Electrical Power & Energy Systems, 33(3), 775–782.

    Article  Google Scholar 

  30. Liu, D. J., & Zou, Z. H. (2012). Water quality evaluation based on improved fuzzy matter-element method. Journal of Environmental Sciences, 24(7), 1210–1216.

    Article  CAS  Google Scholar 

  31. Guo, Y. J. (2007). Theory, method and application of comprehensive evaluation. Beijing: Science Press.

    Google Scholar 

  32. Zeng, M., Xu, W. X., Wei, Y., & Tian, K. (2011). Research on fuzzy synthetic evaluation of renewable distributed energy generation investment. East China Electric Power, 39(2), 0180–0183.

    Google Scholar 

  33. Reza, F. M. (1994). An introduction to information theory. New York: Dover Publications.

    Google Scholar 

  34. Wang, W. (2000). Comparative evaluating of finance and tax policies based on entropy. Quantitative and Technical Economics, 3, 45–48.

    Google Scholar 

  35. MartÍnez-Olvera, C. (2008). Entropy as an assessment tool of supply chain information sharing. European Journal of Operational Research, 185, 405–417.

    Article  Google Scholar 

  36. Johnson, R. A., & Wichern, D. W. (2007). Applied multivariate statistical analysis (Sixth Edition). New Jersey: Upper Saddle River.

    Google Scholar 

  37. Ying, O. Y. (2005). Evaluation of river water quality monitoring stations by principal component analysis. Water Research, 39, 2621–2635.

    Article  Google Scholar 

  38. Pison, G., Rousseeuw, P. J., Filzmoser, P., & Croux, C. (2003). Robust factor analysis. Journal of Multivariate Analysis, 1(84), 145–172.

    Article  Google Scholar 

  39. Chen, C. Z., & Lin, Z. S. (2008). Multiple timescale analysis and factor analysis of energy ecological footprint growth in China 1953–2006. Energy Policy, 36(5), 1666–1678.

    Article  Google Scholar 

  40. You, T. H., & Fan, Z. P. (2002). TOPSIS method for multiple attribute decision making with intervals. Journal of Northeastern University, 23(9), 840–843.

    Google Scholar 

  41. Krohling, R. A., & Campanharo, V. C. (2011). Fuzzy TOPSIS for group decision making: a case study for accidents with oil spill in the sea. Expert Systems with Applications, 38(4), 4190–4197.

    Article  Google Scholar 

  42. Deng, J. L. (1989). Introduction to grey system theory. The Journal of Grey System, 1(1), 1–24.

    Google Scholar 

  43. Liu, S. F., Dang, Y. G., & Fang, Z. G. (2007). The theory and application for grey system (3rd Edition). Beijing: Science Press.

    Google Scholar 

  44. Xu, G., Yang, Y. P., Lu, S. Y., Li, L., & Song, X. N. (2011). Comprehensive evaluation of coal-fired power plants based on grey relational analysis and analytic hierarchy process. Energy Policy, 39(5), 2343–2351.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the editor and the anonymous referees for their valuable comments and suggestions. This work was financially supported by the National Natural Science Foundation of China (51279099), the Special Fund for the Scientific Research on Selection and Training of Shanghai Outstanding Young College Teachers (B211029K), the Science and Technology Committee Foundation of Shanghai Municipality (12ZR1412500), the Innovation Program of Shanghai Municipal Education Commission (13ZZ124), the “Shu Guang” Project of Shanghai Municipal Education Commission and Shanghai Education Development Foundation (12SG40), and the Project of Applied Basic Research of the Ministry of Transportation (2013329810300). A special thank goes to Ms Marie Taris and Dr. Konstantinos Gakis who had reviewed the English of the paper.

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Correspondence to Tingrong Qin.

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Qin, T., Chen, W., Pardalos, P.M. et al. A Combinatorial Assessment Method for the Sequencing Problem and its Application in Waterway Traffic Environments. Environ Model Assess 20, 145–158 (2015). https://doi.org/10.1007/s10666-014-9420-8

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  • DOI: https://doi.org/10.1007/s10666-014-9420-8

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