International Journal of Advances in Applied Computational Intelligence
Journal DOI
2833-5600ISSN (Online)
Volume 4 , Issue 1 , PP: 28-36, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Alber S. Aziz 1 *
Doi: https://doi.org/10.54216/IJAACI.040103
Stakeholders must evaluate the efficiency, profitability, risk management, and overall operational effectiveness of shipping businesses by analyzing their financial and operational performance. Revenue, expenses, fleet utilization, accident rates, market share, and competitive advantage are only a few of the parameters that must be analyzed in this procedure. Stakeholders may use this information to make better choices, spot weak spots, and measure up to competitors. An overview of the criteria used to evaluate the financial and operational performance of shipping businesses is provided in this paper, together with an emphasis on the importance of such assessments in enabling strategic decision-making and long-term development within the shipping sector. This paper used the neutrosophic set framework to overcome the uncertain data. The neutrosophic set combined with the combinative distance-based assessment (CODAS) method to evaluate the financial and operational performance of shipping companies. There are ten criteria and eight companies are used in this study. The application shows the results of the proposed method.
Neutrosophic Set , CODAS Method , Shipping Companies , Evaluation Problem
[1] N. Y. H. Choi and S. Yoshida, “Evaluation of M&A effects in Japanese shipping companies: Case study of NYK & Showa Line and OSK & Navix Line,” Asian J. Shipp. Logist., vol. 29, no. 1, pp. 23–42, 2013.
[2] C. Lu, “Evaluating key resources and capabilities for liner shipping services,” Transp. Rev., vol. 27, no. 3, pp. 285–310, 2007.
[3] K. G. Kitonga, “The relationship between financial management practices and financial performance in the shipping industry in Kenya,” Unpubl. MBA Proj. Univ. Nairobi, Kenya, 2013.
[4] C.-L. Liu and A. C. Lyons, “An analysis of third-party logistics performance and service provision,” Transp. Res. Part E Logist. Transp. Rev., vol. 47, no. 4, pp. 547–570, 2011.
[5] G. Zhou, H. Min, C. Xu, and Z. Cao, “Evaluating the comparative efficiency of Chinese third‐party logistics providers using data envelopment analysis,” Int. J. Phys. Distrib. Logist. Manag., vol. 38, no. 4, pp. 262–279, 2008.
[6] H. Omrani and M. Keshavarz, “A performance evaluation model for supply chain of shipping company in Iran: an application of the relational network DEA,” Marit. Policy Manag., vol. 43, no. 1, pp. 121–135, 2016.
[7] P. T.-W. Lee, C.-W. Lin, and S.-H. Shin, “Financial performance evaluation of shipping companies using entropy and grey relation analysis,” Multi-Criteria Decis. Mak. Marit. Stud. Logist. Appl. Cases, pp. 219–247, 2018.
[8] P. T.-W. Lee, C.-W. Lin, and S.-H. Shin, “A comparative study on financial positions of shipping companies in Taiwan and Korea using entropy and grey relation analysis,” Expert Syst. Appl., vol. 39, no. 5, pp. 5649–5657, 2012.
[9] P. F. Lee, W. S. Lam, and W. H. Lam, “Performance Evaluation of the Efficiency of Logistics Companies with Data Envelopment Analysis Model,” Mathematics, vol. 11, no. 3, p. 718, 2023.
[10] P. M. Panayides, N. Lambertides, and C. S. Savva, “The relative efficiency of shipping companies,” Transp. Res. Part E Logist. Transp. Rev., vol. 47, no. 5, pp. 681–694, 2011.
[11] A. Bhattacharya and D. A. David, “An empirical assessment of the operational performance through internal benchmarking: a case of a global logistics firm,” Prod. Plan. Control, vol. 29, no. 7, pp. 614–631, 2018.
[12] Ahmed Abdelaziz,Alia N. Mahmoud Nova, Car Sharing Station Choice by using Interval Valued Neutrosophic WASPAS Method, International Journal of Advances in Applied Computational Intelligence, Vol. 2 , No. 2 , (2022) : 27-36 (Doi : https://doi.org/10.54216/IJAACI.020203)
[13] S. Broumi, M. Talea, F. Smarandache, and A. Bakali, “Decision-making method based on the interval valued neutrosophic graph,” in 2016 Future Technologies Conference (FTC), IEEE, 2016, pp. 44–50.
[14] S. Broumi, M. Talea, A. Bakali, and F. Smarandache, “Interval valued neutrosophic graphs,” Crit. Rev. XII, vol. 2016, pp. 5–33, 2016.
[15] R. S. U. Haq, M. Saeed, N. Mateen, F. Siddiqui, and S. Ahmed, “An interval-valued neutrosophic based MAIRCA method for sustainable material selection,” Eng. Appl. Artif. Intell., vol. 123, p. 106177, 2023.
[16] M. Yazdani, A. E. Torkayesh, Ž. Stević, P. Chatterjee, S. A. Ahari, and V. D. Hernandez, “An interval valued neutrosophic decision-making structure for sustainable supplier selection,” Expert Syst. Appl., vol. 183, p. 115354, 2021.
[17] H. Zhang, J. Wang, and X. Chen, “An outranking approach for multi-criteria decision-making problems with interval-valued neutrosophic sets,” Neural Comput. Appl., vol. 27, pp. 615–627, 2016.
[18] E. Bolturk and C. Kahraman, “A novel interval-valued neutrosophic AHP with cosine similarity measure,” Soft Comput., vol. 22, pp. 4941–4958, 2018.
[19] D. Sasikala and B. Divya, “A Newfangled Interpretation on Fermatean Neutrosophic Dombi Fuzzy Graphs,” Neutrosophic Syst. with Appl., vol. 7, pp. 36–53, 2023.
[20] S. Albertijn, W. Bessler, and W. Drobetz, “Financing shipping companies and shipping operations: A risk‐management perspective,” J. Appl. Corp. Financ., vol. 23, no. 4, pp. 70–82, 2011.
[21] A. Alizadeh and N. Nomikos, Shipping derivatives and risk management. Springer, 2009.
[22] M. G. Kavussanos, A. Juell-Skielse, and M. Forrest, “International comparison of market risks across shipping-related industries,” Marit. Policy Manag., vol. 30, no. 2, pp. 107–122, 2003.
[23] M. Bloor, M. Thomas, and T. Lane, “Health risks in the global shipping industry: an overview,” Health. Risk Soc., vol. 2, no. 3, pp. 329–340, 2000.
[24] W. Drobetz, D. Schilling, and L. Tegtmeier, “Common risk factors in the returns of shipping stocks,” Marit. Policy Manag., vol. 37, no. 2, pp. 93–120, 2010.
[25] C.-H. Chang, J. Xu, J. Dong, and Z. Yang, “Selection of effective risk mitigation strategies in container shipping operations,” Marit. Bus. Rev., vol. 4, no. 4, pp. 413–431, 2019.
[26] C.-H. Chang, J. Xu, and D.-P. Song, “Risk analysis for container shipping: from a logistics perspective,” Int. J. Logist. Manag., vol. 26, no. 1, pp. 147–171, 2015.
[27] L. Beveridge, M. Fournier, F. Lasserre, L. Huang, and P.-L. Têtu, “Interest of Asian shipping companies in navigating the Arctic,” Polar Sci., vol. 10, no. 3, pp. 404–414, 2016.
[28] S. Nguyen, P. S.-L. Chen, and Y. Du, “Container shipping operational risks: an overview of assessment and analysis,” Marit. Policy Manag., vol. 49, no. 2, pp. 279–299, 2022.
[29] E. Bolturk and A. Karasan, “Interval valued neutrosophic CODAS method for renewable energy selection,” in Data Science and Knowledge Engineering for Sensing Decision Support: Proceedings of the 13th International FLINS Conference (FLINS 2018), World Scientific, 2018, pp. 1026–1033.
[30] A. Karaşan, E. Boltürk, and C. Kahraman, “A novel neutrosophic CODAS method: Selection among wind energy plant locations,” J. Intell. Fuzzy Syst., vol. 36, no. 2, pp. 1491–1504, 2019.
[31] A. Menekse and H. C. Akdag, “A novel interval-valued spherical fuzzy CODAS: Reopening readiness evaluation of academic units in the era of COVID-19,” J. Intell. Fuzzy Syst., no. Preprint, pp. 1–16, 2022.
