Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2023, Cilt: 10 Sayı: 3, 132 - 142, 30.09.2023

Mehmet Yaşar

https://doi.org/10.17261/Pressacademia.2023.1819

Öz

Kaynakça

  • Alinezhad, A., Khalili, J. (2019). WASPAS Method. In: New Methods and Applications in Multiple Attribute Decision Making (MADM). International Series in Operations Research & Management Science, vol 277. Springer, Cham. https://doi.org/10.1007/978-3-030-15009-9_13
  • Arslan, T. (2009). A hybrid model of fuzzy and AHP for handling public assessments on transportation projects. Transportation, 36, 97–112.
  • Barfod, M. B., Salling, K. B. (2015). A new composite decision support framework for strategic and sustainable transport appraisals. Transportation Research Part A: Policy and Practice, 72, 1-15.
  • Cavone, G., Dotoli, M., Epicoco, N. & Seatzu, C. (2017). Intermodal terminal planning by petri nets and data envelopment analysis. Control Engineering Practice, 69, 9-22.
  • Cavone, G., Dotoli, M., Epicoco, N. & Seatzu, C. (2018). Efficient resource planning of intermodal terminals under uncertainty. IFAC-PapersOnLine, 51(9), 398-403.
  • Chakraborty, S., Zavadskas, E. K. (2014). Applications of WASPAS Method in Manufacturing Decision Making. Informatica, 25(1), 1-20.
  • Chakraborty, S., Zavadskas, E. K. & Antucheviciene, J. (2015). Applications of WASPAS method as a multi-criteria decision-making tool. Economic Computation and Economic Cybernetics Studies and Research, 49(1), 5-22.
  • Costescu, D. (2018). Modal competition and complementarity: cost optimization at end-user level. Romanian Journal of Transport Infrastructure, 7(2), 61-76.
  • Fearnley, N., Currie, G., Flügel, S., Gregersen, F. A., Killi, M., Toner, J. & Wardman, M. (2018). Competition and substitution between public transport modes. Research in Transportation Economics, 69, 51-58.
  • Hey, C., Hijkamp, P., Rienstra, S. A. & Rothenberger, D. (1999). Assessing scenarios on European transport policies by means of multicriteria analysis. In: new contrıbutıons to transportation analysis in Europe. Atmospheric Environment, 33, 171-191.
  • Hansson, J., Månsson, S., Brynolf, S. & Grahn, M. (2019). Alternative marine fuels: Prospects based on multi-criteria decision analysis involving Swedish stakeholders. Biomass and Bioenergy, 126, 159-173.
  • Hickman, R., Saxena, S., Banister, D. & Ashiru, O. (2012). Examining transport futures with scenario analysis and MCA. Transportation Research Part A: Policy and Practice, 46(3), 560-575.
  • Giuliano, G. (1985). A multicriteria method for transportation investment planning. Transportation Research Part A: General, 19(1), 29-41.
  • Janic, M. (2003). Multicriteria evaluation of high-speed rail, transrapid maglev and air passenger transport in Europe. Transportation Planning and Technology, 26(6), 491-512.
  • Karleuša, B., Dragičević, N. & Deluka-Tibljaš, A. (2013). Review of multicriteria-analysis methods application in decision making about transport infrastructure. GRAĐEVINAR, 65(7), 619-631.
  • Kopytov,E. & Abramov,D. (2012). Multiple-Criteria Analysis and Choice of Transportation Alternatives in Multimodal Freight Transport System. Transport and Telecommunication Journal, 13(2) 148-158.
  • Liu, L., Zhou, J., An, X., Zhang, Y. & Yang, L. (2010). Using fuzzy theory and information entropy for water quality assessment in Three Gorges region, China. Expert Systems with Applications, 37(3), 2517-2521.
  • Macharis, C., Bernardini, A. (2015). Reviewing the use of Multi-Criteria Decision Analysis for the evaluation of transport projects: Time for a multi-actor approach. Transport Policy, 37, 177-186.
  • Ramani, T. L., Quadrifoglio, L. & Zietsman, J. (2010). Accounting for nonlinearity in the MCDM approach for a transportation planning application. IEEE Transactions on Engineering Management, 57(4), 702-710.
  • Sawadogo, M. & Anciaux, D. (2011). Intermodal transportation within the green supply chain: an approach based on ELECTRE method. International Journal of Business Performance and Supply Chain Modelling, 3(1), 43-65.
  • Shelton, J., Medina, M. (2010). ıntegrated multiple-criteria decision-making method to prioritize transportation projects. Transportation Research Record, 2174(1), 51–57. https://doi.org/10.3141/2174-08
  • Sirikijpanichkul, A., Winyoopadit, S., Jenpanitsub, A. (2017). A multi-actor multi-criteria transit system selection model: A case study of Bangkok feeder system. Transportation Research Procedia, 25, 3736-3755.
  • Stoycheva, S., Marchese, D., Paul, C., Padoan, S., Juhmani, A. S. & Linkov, I. (2018). Multi-criteria decision analysis framework for sustainable manufacturing in automotive industry. Journal of Cleaner Production, 187, 257-272.
  • Tsamboulas, D., Kopsacheili, A. G. (2003). Methodological Framework for Strategic Assessment of Transportation Policies: Application for Athens 2004 Olympic Games. Transportation Research Record, 1848(1), 19–28.
  • Vilakazi, T. (2018). The causes of high intra-regional road freight rates for food and commodities in southern africa. Development Southern Africa, 35(3), 388-403.
  • Wang, T., Lee, H. & Chang, M. C. (2005). A Fuzzy TOPSIS Approach With Entropy Measure for Decision-Making Problem. IEEE International Conference on Industrial Engineering and Engineering Management. Singapore: IEEM.
  • Wang, T.-C., Lee, H.-D. (2009). Developing a Fuzzy TOPSIS Approach Based on Subjective Weights and Objective Weights. Expert Systems with Applications, 36(5), 8980-8985.
  • Wu, J., Liang , L. & Zha, Y. (2011). Determination of weights for ultimate cross efficiency using shannon entropy. Expert Systems with Applications, 38(5), 5162-5165.
  • Yannis, G., Kopsacheili, A., Dragomanovits, A. & Petraki, V. (2020). State-of-the-art review on multi-criteria decision-making in the transport sector. Journal of Traffic and Transportation Engineering, 7(4), 413-431.
  • Zou, Z. H., Yi, Y. & Sun, J. N. (2006). Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. Journal of Environmental sciences, 18(5), 1020-1023.

DETERMINATION OF THE BEST TRANSPORT ALTERNATIVES BY ENTROPY BASED WASPAS METHOD: A COMPARATIVE STUDY ON CROSS-COMPETITIVE ROUTES

Yıl 2023, Cilt: 10 Sayı: 3, 132 - 142, 30.09.2023

Mehmet Yaşar

https://doi.org/10.17261/Pressacademia.2023.1819

Öz

Purpose- Users typically choose the option that is most convenient for them in terms of time or cost based on their preferences when multiple options are presented on the same line. In this regard, users' preferences are significantly impacted by the product components given by competing travel options. The aim of this study is to evaluate the criteria that are considered to be effective in competition and user preferences in transport corridors where there is cross-competition, and to rank the routes according to these criteria.
Methodology- In this context, transport corridors in Turkey and some European countries have been selected. The criteria evaluation of the selected routes was carried out using the Entropy method and then the ranking of the routes was carried out using the WASPAS method.
Findings- It can be seen that the London-Manchester air route is ranked first, while the Paris-Lyon air route is ranked last in the study. Taking into account the HSR ranking, the London-Manchester corridor is in first place, as in the airline sector. Among the selected routes, the Ankara-Istanbul HSR corridor is ranked the last. When it comes to bus transport, the Ankara-Istanbul route is in the top position. On the other hand, the Berlin-Frankfurt corridor comes last.
Conclusion- The results of the research are important in terms of understanding the factors that are effective in cross-competition By considering the performance criteria of these routes and their respective weightings, it can inform decisions related to the regulation of fares or the development of investment programmes to enhance the competitiveness of public transport modes such as High Speed Rail (HSR). In essence, this research fills a gap in transport decision studies by providing a comprehensive analysis of routes across all modes and providing actionable insights for policy makers.

Anahtar Kelimeler

Cross competition transportation modes airline WASPAS entropy

Kaynakça

  • Alinezhad, A., Khalili, J. (2019). WASPAS Method. In: New Methods and Applications in Multiple Attribute Decision Making (MADM). International Series in Operations Research & Management Science, vol 277. Springer, Cham. https://doi.org/10.1007/978-3-030-15009-9_13
  • Arslan, T. (2009). A hybrid model of fuzzy and AHP for handling public assessments on transportation projects. Transportation, 36, 97–112.
  • Barfod, M. B., Salling, K. B. (2015). A new composite decision support framework for strategic and sustainable transport appraisals. Transportation Research Part A: Policy and Practice, 72, 1-15.
  • Cavone, G., Dotoli, M., Epicoco, N. & Seatzu, C. (2017). Intermodal terminal planning by petri nets and data envelopment analysis. Control Engineering Practice, 69, 9-22.
  • Cavone, G., Dotoli, M., Epicoco, N. & Seatzu, C. (2018). Efficient resource planning of intermodal terminals under uncertainty. IFAC-PapersOnLine, 51(9), 398-403.
  • Chakraborty, S., Zavadskas, E. K. (2014). Applications of WASPAS Method in Manufacturing Decision Making. Informatica, 25(1), 1-20.
  • Chakraborty, S., Zavadskas, E. K. & Antucheviciene, J. (2015). Applications of WASPAS method as a multi-criteria decision-making tool. Economic Computation and Economic Cybernetics Studies and Research, 49(1), 5-22.
  • Costescu, D. (2018). Modal competition and complementarity: cost optimization at end-user level. Romanian Journal of Transport Infrastructure, 7(2), 61-76.
  • Fearnley, N., Currie, G., Flügel, S., Gregersen, F. A., Killi, M., Toner, J. & Wardman, M. (2018). Competition and substitution between public transport modes. Research in Transportation Economics, 69, 51-58.
  • Hey, C., Hijkamp, P., Rienstra, S. A. & Rothenberger, D. (1999). Assessing scenarios on European transport policies by means of multicriteria analysis. In: new contrıbutıons to transportation analysis in Europe. Atmospheric Environment, 33, 171-191.
  • Hansson, J., Månsson, S., Brynolf, S. & Grahn, M. (2019). Alternative marine fuels: Prospects based on multi-criteria decision analysis involving Swedish stakeholders. Biomass and Bioenergy, 126, 159-173.
  • Hickman, R., Saxena, S., Banister, D. & Ashiru, O. (2012). Examining transport futures with scenario analysis and MCA. Transportation Research Part A: Policy and Practice, 46(3), 560-575.
  • Giuliano, G. (1985). A multicriteria method for transportation investment planning. Transportation Research Part A: General, 19(1), 29-41.
  • Janic, M. (2003). Multicriteria evaluation of high-speed rail, transrapid maglev and air passenger transport in Europe. Transportation Planning and Technology, 26(6), 491-512.
  • Karleuša, B., Dragičević, N. & Deluka-Tibljaš, A. (2013). Review of multicriteria-analysis methods application in decision making about transport infrastructure. GRAĐEVINAR, 65(7), 619-631.
  • Kopytov,E. & Abramov,D. (2012). Multiple-Criteria Analysis and Choice of Transportation Alternatives in Multimodal Freight Transport System. Transport and Telecommunication Journal, 13(2) 148-158.
  • Liu, L., Zhou, J., An, X., Zhang, Y. & Yang, L. (2010). Using fuzzy theory and information entropy for water quality assessment in Three Gorges region, China. Expert Systems with Applications, 37(3), 2517-2521.
  • Macharis, C., Bernardini, A. (2015). Reviewing the use of Multi-Criteria Decision Analysis for the evaluation of transport projects: Time for a multi-actor approach. Transport Policy, 37, 177-186.
  • Ramani, T. L., Quadrifoglio, L. & Zietsman, J. (2010). Accounting for nonlinearity in the MCDM approach for a transportation planning application. IEEE Transactions on Engineering Management, 57(4), 702-710.
  • Sawadogo, M. & Anciaux, D. (2011). Intermodal transportation within the green supply chain: an approach based on ELECTRE method. International Journal of Business Performance and Supply Chain Modelling, 3(1), 43-65.
  • Shelton, J., Medina, M. (2010). ıntegrated multiple-criteria decision-making method to prioritize transportation projects. Transportation Research Record, 2174(1), 51–57. https://doi.org/10.3141/2174-08
  • Sirikijpanichkul, A., Winyoopadit, S., Jenpanitsub, A. (2017). A multi-actor multi-criteria transit system selection model: A case study of Bangkok feeder system. Transportation Research Procedia, 25, 3736-3755.
  • Stoycheva, S., Marchese, D., Paul, C., Padoan, S., Juhmani, A. S. & Linkov, I. (2018). Multi-criteria decision analysis framework for sustainable manufacturing in automotive industry. Journal of Cleaner Production, 187, 257-272.
  • Tsamboulas, D., Kopsacheili, A. G. (2003). Methodological Framework for Strategic Assessment of Transportation Policies: Application for Athens 2004 Olympic Games. Transportation Research Record, 1848(1), 19–28.
  • Vilakazi, T. (2018). The causes of high intra-regional road freight rates for food and commodities in southern africa. Development Southern Africa, 35(3), 388-403.
  • Wang, T., Lee, H. & Chang, M. C. (2005). A Fuzzy TOPSIS Approach With Entropy Measure for Decision-Making Problem. IEEE International Conference on Industrial Engineering and Engineering Management. Singapore: IEEM.
  • Wang, T.-C., Lee, H.-D. (2009). Developing a Fuzzy TOPSIS Approach Based on Subjective Weights and Objective Weights. Expert Systems with Applications, 36(5), 8980-8985.
  • Wu, J., Liang , L. & Zha, Y. (2011). Determination of weights for ultimate cross efficiency using shannon entropy. Expert Systems with Applications, 38(5), 5162-5165.
  • Yannis, G., Kopsacheili, A., Dragomanovits, A. & Petraki, V. (2020). State-of-the-art review on multi-criteria decision-making in the transport sector. Journal of Traffic and Transportation Engineering, 7(4), 413-431.
  • Zou, Z. H., Yi, Y. & Sun, J. N. (2006). Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. Journal of Environmental sciences, 18(5), 1020-1023.
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Bölüm Articles
Yazarlar

Mehmet Yaşar 0000-0001-7237-4069

Yayımlanma Tarihi 30 Eylül 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 10 Sayı: 3

Kaynak Göster

APA Yaşar, M. (2023). DETERMINATION OF THE BEST TRANSPORT ALTERNATIVES BY ENTROPY BASED WASPAS METHOD: A COMPARATIVE STUDY ON CROSS-COMPETITIVE ROUTES. Journal of Management Marketing and Logistics, 10(3), 132-142. https://doi.org/10.17261/Pressacademia.2023.1819

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