Evaluating the Performance of Railway Transportation Companies Using Multi-Criteria Decision-Making Methods

Year 2024, , 11 - 24, 31.07.2024

Çağdaş Yüksel , Nuşin Uncu

https://doi.org/10.47072/demiryolu.1407420

Abstract

The purpose of performance evaluation is to generate measurable data on an organization's performance, with the goal of assisting managerial decision-making and enhancing overall performance. In this study, the key performance indicators (KPIs) for railway transportation companies are identified based on expert opinions and previous frameworks. The operational performance of various railway freight transport companies was evaluated using multi-criteria decision-making methods (MCDM). Among the MCDM approaches, the Evaluation Based on Distance from Average Solution (EDAS) method was applied as the main method. In addition to the EDAS method, alternative MCDM methods such as TOPSIS, PROMETHEE II, and COPRAS were used to highlight potential deviations when compared to the results obtained with the EDAS method. Based on the research findings, three out of the seven KPIs, namely safety, have the highest weight at 38%, followed by punctuality at 19%, and journey time at 12%. Subsequently, companies were ranked according to their performance based on all KPIs. Furthermore, a sensitivity analysis was conducted to demonstrate how changes in the relative weights of KPIs can affect the results.

Keywords

Key performance indicators, Multi-criteria decision making, Performance evaluation, Railway freight transport

Demiryolu Taşımacılığı Firmalarının Performanslarının Çok Kriterli Karar Verme Yöntemleri ile Değerlendirilmesi

Abstract

Performans değerlendirmenin amacı, bir organizasyonun performansıyla ilgili ölçülebilir veriler üretmek, yönetimsel karar alma sürecine destek olmak ve genel performansı artırmaktır. Bu çalışmada, demiryolu taşımacılığı şirketleri için anahtar performans göstergeleri (APG'ler), uzman görüşleri ve önceki çerçevelere dayalı olarak belirlenmektedir. Çeşitli demiryolu yük taşıma şirketlerinin işletme performansı, çoklu kriterli karar verme yöntemleri (ÇKKV) kullanılarak değerlendirilmektedir. ÇKKV yaklaşımlarından biri olan Ortalama Çözüm Uzaklığına Dayalı Değerlendirme (EDAS) yöntemi, ana yöntem olarak uygulanmıştır. EDAS yöntemiyle elde edilen sonuçlarla karşılaştırıldığında potansiyel sapmaları göstermek için TOPSIS, PROMETHEE II ve COPRAS gibi alternatif ÇKKV yöntemleri de kullanılmıştır. Araştırma bulgularına göre, yedi APG'den üçü, yani emniyet %38 ile en yüksek ağırlığa sahipken, bunu %19 ile dakiklik ve %12 ile seyir süresi takip etmektedir. Daha sonra şirketler tüm KPI'lar baz alınarak performanslarına göre sıralanmıştır. Ayrıca, APG'lerin göreceli ağırlıklarındaki değişikliklerin sonuçları nasıl etkileyebileceğini göstermek için duyarlılık analizi yapılmıştır.

Keywords

Anahtar Performans Göstergeleri (APGler), Çok kriterli karar verme, Performans değerlendirme, Demiryolu yük taşımacılığı

References

• [1] M. Frost, S. G. Ison, and R. Watson, “UK rail transport: a review of demand and supply,” Proceedings of the ICE: Transport, vol. 165, no. 3, pp. 225-234, 2012
• [2] TCDD, “Turkish State Railways sectoral report,” Turkey, 2020
• [3] IEA, “The future of ail: Opportunities for energy and the environment”, Paris, 2019
• [4] M. Lu, “Evaluation of railway performance through quality of service,” Ph.D. dissertation, University of Birmingham, England, 2016
• [5] UIC leaflet 406: capacity, UIC International Union of Railways, France, 2004
• [6] R. J. Anderson, R. Hirsch, M. Trompet, and W. Adeney, “Developing benchmarking methodologies for railway infrastructure management companies. Railway Technology Strategy Centre, Centre for Transport Studies, London, United Kingdom, 2003
• [7] IMPROVERAIL, “Improved tools for railway capacity and access management: D2 benchmarking methodologies a uropa zationion of concepts in the railway sector,” Competitive and Sustainable Growth Programme, European Commission, Belgium, 2003
• [8] European Commission, “Infrastructure managers (PRIME)”, 2023. [Online]. Available: https://transport.ec.europa.eu/transport-modes/rail/market/infrastructure-managers-prime_en [Accessed August 12, 2023]
• [9] Key performance indicators for performance benchmarking, PRIME Catalogue, 2019
• [10] A. Cebeci, H. Tüydeş-Yaman, and D. M. Z. Islam, “Spatial distribution of the rail freight demand in Turkey prior to railway reform,” Research in Transportation Business and Management, vol. 44, 2022
• [11] UAB-Ministry of Transport and Infrastructure, “Demiryolu tren işletmecisi yetki belgesi sahibi firmalar,” 2020. [Online]. Available: https://uhdgm.uab.gov.tr/uploads/pages/demiryolu-tasimaciligi-yetki-belgesi-almis-olan fi/demiryolu-tren-isletmecisi-yetki-belgesi-sahibi-firmalar.pdf
• [12] Y. He, F. Lei, G. Wei, R. Wang, J. Wu, and C. Wei, “EDAS method for multiple attribute group decision making with probabilistic uncertain linguistic information and its application to green supplier selection,” International Journal of Computational Intelligence Systems, vol. 12, no. 2, pp. 1361-1370, 2019
• [13] M. Keshavarz Ghorabaee, E. K. Zavadskas, L. Olfat, and Z. Turskis, “Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS),” Informatica, vol. 26, no. 3, pp. 435-451, 2015
• [14] G. F. Frederico, and V. Cavenaghi, “Measuring performance in rail freight transportation companies,” International Business Research, vol. 10, no. 11, pp. 117-128, 2017
• [15] L. Zhang, Q. Cheng, and Q. Siyuan, “Evaluation of railway transportation performance based on CRITIC-Relative Entropy Method in China,” Journal of Advanced Transportation, vol. 2023, 2023
• [16] R. Ranjan, P. Chatterjee, and S. Chakraborty, “Performance evaluation of Indian Railway zones using DEMATEL and VIKOR methods,” Benchmarking: An International Journal, vol. 23, no. 1, pp. 78-95, 2016
• [17] E. Jose, P. Agarwal, J. Zhuang, and J. Swaminathan, “A multi-criteria decision making approach to evaluating the performance of Indian railway zones,” Annals of Operations Research, vol. 325, no. 2, pp. 1133-1168, 2023
• [18] N. Petrović, J. Mihajlović, V. Jovanović, D. Ćirić, and T. Živojinović, “Evaluating annual operation performance of Serbian railway system by using multiple criteria decision-making technique” Acta Polytechnica Hungarica, vol. 20, no. 1, 2023
• [19] A. Fraszczyk, T. Lamb, and M. Marinov, “Are railways really that bad? An evaluation of rail systems performance in Europe with a focus on passenger rail,” Transportation Research Part A: Policy and Practice, vol. 94, pp. 573–591, 2016
• [20] S. Stoilova, N. Munier, M. Kendra, and T. Skrúcaný, “Multi-criteria evaluation of railway network performance in countries of the TEN-T orient-east med corridor,” Sustainability, vol. 12, no. 4, pp. 1482, 2020
• [21] S. D. Stoilova, “A multi-criteria assessment approach for the evaluation of railway transport in the Balkan region,” Promet-Traffic&Transportation, vol. 31, no. 6, pp. 655-668, 2019
• [22] K. Kara, and G. C. Yalçın, “Assessing railway transportation performance of European countries with CRITIC and ROV techniques” Demiryolu Mühendisliği, no. 17, pp. 93-106, 2023
• [23] M. B. Bouraima, A. Saha, A. Stević, Z. Antucheviciene, J. Qiu, and P. Marton, “Assessment actions for improving railway sector performance using intuitionistic fuzzy-rough multi-criteria decision-making model,” Applied Soft Computing, vol. 148, 2023
• [24] M. G. Sharma, R. M. Debnath, R. Oloruntoba, and S. M. Sharma, “Benchmarking of rail transport service performance through DEA for Indian railways,” The International Journal of Logistics Management, vol. 27, no. 3, pp. 629-649, 2016
• [25] N. O. Olsson, and H. Haugland, “Influencing factors on train punctuality - results from some Norwegian studies,” Transport policy, vol. 11, no. 4, pp. 387-397, 2004
• [26] B. D. Dağıdır, and B. Özkan, “A comprehensive evaluation of a company performance using sustainability balanced scorecard based on picture fuzzy AHP,” Journal of Cleaner Production, vol. 435, no. 140519, 2024
• [27] N. H. Zardari, K. Ahmed, S. M. Shirazi, and Z. B. Yusop, “Weighting methods and their effects on multi-criteria decision-making model outcomes in water resources management,” Springer International Publishing, Switzerland, pp. 1-5, 2014
• [28] A. Özdağoğlu, and G. Özdağoğlu, “Comparison of AHP and fuzzy AHP for the multi-criteria decision making processes with linguistic evaluations,” İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, vol. 6, no. 11, pp. 65-85, 2007
• [29] J. J. Buckley, “Fuzzy hierarchical analysis,” Fuzzy sets and systems, vol. 17, no. 3, pp. 233-247, 1985
• [30] M. B. Ayhan, “A fuzzy AHP approach for supplier selection problem: A case study in a gear motor company,” International Journal of Managing Value and Supply Chains, vol. 4, pp. 11-23, 2013
• [31] M. Mathew, and S. Sahu, “Comparison of new multi-criteria decision making methods for material handling equipment selection,” Management Science Letters, vol. 8, no. 3, pp. 139–150, 2018
• [32] N. Yalçın, and N. Uncu, “Applying EDAS as an applicable MCDM method for industrial robot selection,” Sigma Journal of Engineering and Natural Sciences, vol. 37, no. 3, pp. 779-796, 2019
• [33] A. Zaki Mohamed Noor et al., “Fusion of fuzzy AHP in selecting material for drinking water bottle based on customer needs,” Journal of Engineering and Applied Sciences, vol. 12, no. 14, pp. 4243-4249, 2017
• [34] S. Razavi et al., “Future of sensitivity analysis: An essential discipline for systems modeling and policy support,” Environmental Modelling and Software, vol. 137, 2021
• [35] B. Iooss, and A. Saltelli, “Introduction to sensitivity analysis,” in Handbook of Uncertainty Quantification, Springer, Cham, pp. 1-31, 2017