ASSESSMENT OF THE PERFORMANCE OF LOGISTICS VILLAGES OPERATED BY THE TURKISH STATE RAILWAYS USING MCDM AND DEA METHODS

Year 2022, Volume: 22 Issue: 2, 169 - 182, 28.04.2022

Fatma Gül Altın , Taner Filiz

https://doi.org/10.21121/eab.983220

Cited By: 2

Abstract

Increasing competition with globalization brought along the cost problem arising from logistics activities. In this context, logistics villages play an active role in reducing costs. Logistics villages provide significant benefits to users as areas where goods from different modes of transport are transferred, arranged and prepared for transportation, and all logistics-related activities are gathered in one region. Logistics villages have begun to be established by The Turkish State Railways (TSR-Türkiye Cumhuriyeti Devlet Demiryolları) using government resources after 2006 in Turkey. The aim of this study is to assess the logistics performances of the eight logistics villages in operation by analyzing their efficiencies using Multi Criteria Decision Making (MCDM) and Data Envelopment Analysis (DEA) methods. In the study, Entropy-based EDAS, MAUT and MOOSRA methods have been used. The efficiency scores of logistics villages were calculated using output-oriented DEA models. According to CCR and BCC models, İstanbul (Halkalı) and Uşak logistics villages were found to be efficient.

Keywords

Logistics Village, Entropy, MAUT, EDAS, MOOSRA, DEA

References

• Ahi, T. (2015). Demiryollarında lojistik merkezler. Demiryolu Mühendisliği, 2, 32-35.
• AMCO. (2018). Why is logistics management important?, Available at: https://www.amcoservices.co.uk/why-is-logistics-management-important/, (Accessed May 12, 2020).
• Baydar, A. M., Süral, H., & Çelik, M. (2017). Freight villages: a literature review from the sustainability and societal equity perspective. Journal of Cleaner Production, 167(20), 1208-1221.
• Bilisik, M. T., & Elibol, G. O. (2017). Efficiency research with total factor productivity and determination of improvement targets. Journal of Business Economics and Finance. 6(3), 246-253.
• Blagojevic, A., Veskovic, S., Kasalica, S., Gojic, A., & Allamani, A. (2020). The application of the fuzzy ahp and dea for measuring the effıciency of freight transport railway undertakings. Operational Research in Engineering Sciences: Theory and Applications. 3(2), 1-23.
• Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
• Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Alternative DEA models. Data envelopment analysis a comprehensive text with models, references, and DEA-solver software. Springer, NY, USA.
• Das, M. C., Sarkar, B., & Ray, S. (2012). Decision making under conflicting environment: a new MCDM method. International Journal of Applied Decision Sciences, 5(2), 142-162.
• Dinc, M., & Haynes, K. E. (1999). Sources of regional inefficiency an integrated shift-share, data envelopment analysis and input-output approach. The Annals of Regional Science, 33(4), 469-489.
• Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253-281.
• Fragoudaki, A., & Giokas, D. (2020). Airport efficiency in the dawn of privatization: the case of Greece. Journal of Air Transport Management, 86, 1-14.
• Ergün, M., Korucuk, S., & Memiş, S. (2020). Sürdürülebilir afet lojistiğine yönelik ideal afet depo yeri seçimi: Giresun ili örneği. Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(1), 144-165.
• Gök Kısa, A., & Ayçin, E., (2019), ''OECD Ülkelerinin Lojistik Performanslarının SWARA Tabanlı EDAS Yöntemi ile Değerlendirilmesi'', Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, C.9, S.1, (301-325).
• Huang, W., Shuai, B., Xu, Y., Zhang, S., & Mao, B. (2019). Railway express freight train service sites planning: a two-stage entropy-TOPSIS approach. Transportmetrica A: Transport Science, 15(2), 807-823.
• Huang, W., Zhang, Y., Yu, Y., Xu, Y., Xu, M., Zhang, R., Deieu, G. J. D., Yin, D., & Liu, Z. (2021). Historical data-driven risk assessment of railway dangerous goods transportation system: comparisons between Entropy weight method and Scatter Degree method. Reliability Engineering & System Safety, 205, 1-8.
• Iyer, K. C., & Nanyam, V.P.S. N. (2021). Technical efficiency analysis of container terminals in India. The Asian Journal of Shipping and Logistics, 37(1), 61-72.
• Karadeniz, V., & Akpınar, E. (2011). Türkiye’de lojistik köy uygulamalari ve yeni bir lojistik köy önerisi. Marmara Coğrafya Dergisi, 24, 49-71.
• Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435-451.
• Khorram, S. (2020). A novel approach for ports’ container terminals’ risk management based on formal safety assessment: FAHP entropy measure-VIKOR model. Natural Hazards, 103, 1671-1707.
• Kuo, K.-C., Lu, W.-M., & Le, M.-H. (2020). Exploring the performance and competitiveness of Vietnam port industry using DEA. The Asian Journal of Shipping and Logistics, 36(3), 136-144.
• Li, J. (2020). The structure of the overall network of research on spatial correlation network structure and effects of regional logistics. Design Engineering, 11, 783-802.
• Loken, E., (2007). Use of multicriteria decision analysis methods for energy planning problems. Renewable and Sustainable Energy Reviews, 11(7), 1584-1595.
• Luptacik, M. (2010). Data envelopment analysis. Mathematical optimization and economic analysis (p.p. 135-186). Springer, NY, USA.
• Maksimovic, M., Brzakovic, M., Grahovac, M., & Jovanovic, I. (2017). An approach for evaluation the safety and quality of transport at the open pit mines, based on the EDAS method. Mining and Metallurgy Engineering Bor, 3-4, 139-144.
• Meidute, I. (2005). Comparative analysis of the definitions of logistics centres. Transport, 20(3), 106-110.
• Mircetic, D., Nikolicic, S., & Maslaric, M. (2014). Logistic centers: Literature review and papers classification. The Fifth International Conference Transport and Logistics.
• Mou, N., Wang, C., Yang, T., & Zhang, L. (2020). Evaluation of development potential of ports in the Yangtze River Delta using FAHP-Entropy model. Sustainability, 12(2), 1-24.
• Özmen, M., & Kızılkaya Aydoğan, E. (2020). Robust multi‑criteria decision making methodology for real life logistics center location problem. Artificial Intelligence Review, 53(1), 725-751.
• Postiguillo, J. R., Campo, J. M. & Santamera, J. A. (2015). Areas of logistics activity evolution and tendencies criteria and parameters of design to implementation and organization. International Journal of Innovation, Management and Technology, 6(2), 126-129.
• Quintano, C., Mazzocchi, P., & Rocca, A. (2020). A competitive analysis of EU ports by fixing spatial and economic dimensions. Journal of Shipping and Trade, 5, 1-19.
• Seiford, L. M., & Thrall, R. M. (1990). Recent developments in DEA: the mathematical programming approach to frontier analysis. Journal of Econometrics, 46(1-2), 7-38.
• Sezen, B., & Gürsev, S. (2014). Türkiye’de kurulması planlanan lojistik merkezler hakkında bir analiz çalışması. Maramara Üniversitesi Öneri Dergisi, 11(42), 105-126.
• Shemshadi, A., Shirazi, H., Toreihi, M., & Tarokh, M. J. (2011). A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting. Expert Systems with Applications, 38(10), 12160-12167.
• 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.
• TCDD (2012). Lojistik Merkezler, Available at: http://www.lojistikhatti.com/haber /2012/09/lojistikmerkezler, (Accessed Feb 25, 2020).
• Theo, N., Francesco, P., Giovanni, S. & Marcello, R., (2017), A taxonomy of logistics centres: overcoming conceptual ambiguity. Transport Reviews, 37(3), 276-299.
• Ulutaş, A., Karaköy, Ç., Arıç, K. H., & Cengiz, E. (2018). Çok kriterli karar verme yöntemleri ile lojistik merkezi yeri seçimi. İktisadi Yenilik Dergisi, 5(2), 45-53.
• Uygun, A. İ. (2019). TCDD Genel Müdürlüğü lojistik merkez projeleri sunumu, Available at: www.tcdd.gov.tr, (Accessed Jun 20, 2020).
• Velasquez, M., & Hester, P.T. (2013). An analysis of multi criteria decision making methods. International Journal of Operations Research, 10(2), 56-66.
• Veskovic, S., Stevic, Z., Karabasevic, D., Rajilic, S., Milinkovic, S., & Stojic, G. (2020). A new integrated fuzzy approach to selecting the best solution for business balance of passenger rail operator: fuzzy PIPRECIA-fuzzy EDAS model. Symmetry, 12(5), 1-20.
• Yürüyen, A. A., & Ulutaş, A. (2020). Bulanık AHP ve bulanık EDAS yöntemleri ile üçüncü parti lojistik firması seçimi, Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi (İİB), 8, 283-294.
• Zafar, I., Wuni, I. Y., Shen, G. Q., Zahoor, H. & Xue, J. (2020). A decision support framework for sustainable highway alignment embracing variant preferences of stakeholders: case of China Pakistan economic corridor, Journal of Environmental Planning and Management, 63(9), 1550-1584.
• Zietsman, J., Rilett, L. R., & Kim, S. J. (2006). Transportation corridor decision-making with multi-attribute utility theory. International Journal of Management and Decision Making, 7(2-3), 254-266.