Multi Criteria Evaluation Framework for Prioritizing Indian Railway Stations Using Modified Rough AHP-Mabac Method

1. Brons, M., Givoni, M., and Rietveld, P. (2009) Access to railway stations and its potential in increasing rail use. Transportation Research Part A, 43, pp. 136-149.10.1016/j.tra.2008.08.002Search in Google Scholar

2. Chen, C.T. (2000) Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems, 114(1), pp. 1-9.10.1016/S0165-0114(97)00377-1Search in Google Scholar

3. Daamen, W. (2002) A quantitative assessment on the design of a railway station. WIT Transactions on the Built Environment, 61.Search in Google Scholar

4. Debnath, A., Roy, J., Kar, S., Zavadskas, E.K. and Antucheviciene, J. (2017) A Hybrid MCDM Approach for Strategic Project Portfolio Selection of Agro By-Products. Sustainability, 9(8), pp. 1302.10.3390/su9081302Search in Google Scholar

5. Givoni, M. and Rietveld, P. (2007) The access journey to the railway station and its role in passengers’ satisfaction with rail travel, Transport Policy 14, pp. 357-365.10.1016/j.tranpol.2007.04.004Open DOISearch in Google Scholar

6. Givoni, M. and Rietveld, P. (2014) Do cities deserve more railway stations? The choice of a departure railway station in a multiple-station region, Journal of Transport Geography, 36, pp. 89-97.10.1016/j.jtrangeo.2014.03.004Open DOISearch in Google Scholar

7. Hennig, C., Meila, M., Murtagh, F. and Rocci, R. (2015) Handbook of cluster analysis. CRC Press.10.1201/b19706Search in Google Scholar

8. Kaakai, F., Hayat, S. and Moudni, A. E. (2007) A hybrid Petri nets-based simulation model for evaluating the design of railway transit stations. Simulation Modelling Practice and Theory, 15, pp. 935-969.10.1016/j.simpat.2007.05.003Search in Google Scholar

9. Khoo, L.P. and Zhai, L.Y. (2001) A prototype genetic algorithm-enhanced rough set-based rule induction system. Computers in Industry, 46(1), pp. 95-106.10.1016/S0166-3615(01)00117-8Search in Google Scholar

10. Lai, W.T. and Chen, C. F. (2011) Behavioral intentions of public transit passengers-The roles of service quality, perceived value, satisfaction and involvement. Transport Policy, 18, pp. 318-325.10.1016/j.tranpol.2010.09.003Search in Google Scholar

11. Liang, H., Ren, J., Gao, Z., Gao, S., Luo, X., Dong, L. and Scipioni, A. (2016) Identification of critical success factors for sustainable development of biofuel industry in China based on grey decision-making trial and evaluation laboratory (DEMATEL). Journal of Cleaner Production, 131, pp. 500-508.10.1016/j.jclepro.2016.04.151Search in Google Scholar

12. Martínez, L., Liu, J., Ruan, D. and Yang, J.B. (2007) Dealing with decision-making information in engineering evaluation processes. Information Sciences, 177(7), pp. 1533-1542.10.1016/j.ins.2006.07.005Search in Google Scholar

13. Mateus, R., Ferreira, J. A. and Carreira, J. (2008) Multicriteria decision analysis (MCDA): Central Porto high-speed railway station. European Journal of Operational Research, 187, pp. 1-18.10.1016/j.ejor.2007.04.006Search in Google Scholar

14. Mohajeri, N. and Amin, G. R. (2010) Railway station site selection using analytical hierarchy process and data envelopment analysis. Computers & Industrial Engineering, 59, pp. 107-114.10.1016/j.cie.2010.03.006Search in Google Scholar

15. Pamučar, D. and Ćirović, G. (2015) The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation Area Comparison (MABAC). Expert Systems with Applications, 42(6), pp. 3016-3028.10.1016/j.eswa.2014.11.057Search in Google Scholar

16. Pamučar, D., Petrovic, I. and Ćirović, G. (2018) Modification of the Best-Worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers, Expert Systems with Applications, 91, pp. 89-106.Search in Google Scholar

17. Pawlak, Z. (1982) Rough sets. International Journal of Parallel Programming, 11(5), pp. 341-356.Zh10.1007/BF01001956Search in Google Scholar

18. Pawlak, Z. (1991) Rough Sets. Theoretical aspects of reasoning about data. Kluwer Academic Publishers, Dordrecht.10.1007/978-94-011-3534-4_7Search in Google Scholar

19. Qazi, K.I., Lam, H.K., Xiao, B., Ouyang, G. and Yin, X. (2016) Classification of epilepsy using computational intelligence techniques. CAAI Transactions on Intelligence Technology, 1(2), pp. 137-149.10.1016/j.trit.2016.08.001Search in Google Scholar

20. Rietveld, P. (2000). The accessibility of railway stations: the role of the bicycle in The Netherlands, Transportation Research Part D, 5, pp. 71-75.Search in Google Scholar

21. Roy, J., Adhikary, K. and Kar, S. (2016a) Credibilistic TOPSIS Model for Evaluation and Selection of Municipal Solid Waste Disposal Method. ArXiv preprint, URL: http://arxiv.org/abs/1607.01254.Search in Google Scholar

22. Roy, J., Chatterjee, K., Bandyopadhyay, A. and Kar, S. (2018) Evaluation and selection of medical tourism sites: A rough analytic hierarchy process based multi‐attributive border approximation area comparison approach. Expert Systems, 35(1), DOI: https://doi.org/10.1111/exsy.1223210.1111/exsy.12232Open DOISearch in Google Scholar

23. Roy, J., Ranjan, A., Debnath, A. and Kar. S. (2016b) An extended MABAC for multi-attribute decision making using trapezoidal interval type-2 fuzzy numbers. ArXiv preprint, URL: http://arxiv.org/abs/1607.01254Search in Google Scholar

24. Saaty, T.L. and Vargas, L.G. (2012) Models, methods, concepts & applications of the analytic hierarchy process (Vol. 175). Springer Science & Business Media.10.1007/978-1-4614-3597-6Search in Google Scholar

25. Song, W., Ming, X. and Wu, Z. (2013) An integrated rough number-based approach to design concept evaluation under subjective environments. Journal of Engineering Design, 24(5), pp. 320-341.10.1080/09544828.2012.732994Open DOISearch in Google Scholar

26. Wang, H., Yang, B. and Chen, W. (2016) Unknown constrained mechanisms operation based on dynamic interactive control. CAAI Transactions on Intelligence Technology, 1(3), pp. 259-271.10.1016/j.trit.2016.10.004Search in Google Scholar

27. Zheng, P., Xu, X. and Xie, S.Q. (2016) A weighted interval rough number based method to determine relative importance ratings of customer requirements in QFD product planning. Journal of Intelligent Manufacturing, pp. 1-14. DOI: https://doi.org/10.1007/s10845-016-1224-z10.1007/s10845-016-1224-zOpen DOISearch in Google Scholar

28. Xue, Y.X., You, J.X., Lai, X.D. and Liu, H.C. (2016) An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information. Applied Soft Computing, 38, pp. 703-713.10.1016/j.asoc.2015.10.010Search in Google Scholar

29. Zemp, S., Stauffacher, M., Lang, D.J. and Scholz, R.W. (2011) Classifying railway stations for strategic transport and land use planning: Context matters. Journal of Transport Geography, 19, pp. 670-679.10.1016/j.jtrangeo.2010.08.008Open DOISearch in Google Scholar

30. Zhai, L.Y., Khoo, L.P. and Zhong, Z.W. (2008) A rough set enhanced fuzzy approach to quality function deployment. The International Journal of Advanced Manufacturing Technology, 37(5-6), pp. 613-624.10.1007/s00170-007-0989-9Open DOISearch in Google Scholar

31. Zhai, L.Y., Khoo, L.P. and Zhong, Z.W. (2009) A rough set based QFD approach to the management of imprecise design information in product development. Advanced Engineering Informatics, 23(2), pp. 222-228.10.1016/j.aei.2008.10.010Open DOISearch in Google Scholar

32. Zhai, L.Y., Khoo, L.P. and Zhong, Z.W. (2010) Towards a QFD-based expert system: A novel extension to fuzzy QFD methodology using rough set theory. Expert Systems with Applications, 37(12), 8888-8896.10.1016/j.eswa.2010.06.007Open DOISearch in Google Scholar

33. Zhu, G. N., Hu, J., Qi, J., Gu, C.C. and Peng, Y.H. (2015) An integrated AHP and VIKOR for design concept evaluation based on rough number. Advanced Engineering Informatics, 29(3), pp. 408-418.10.1016/j.aei.2015.01.010Search in Google Scholar