SavedAdd your journal

Journals / Mersin Üniversitesi Denizcilik ve Lojistik Araştırmaları Dergisi / 2025 / Cilt: 7 Sayı: 2

Operational Efficiency and Methodological Diversity in Container Port Performance Assessment

Konteyner Limanı Performans Değerlendirmesinde Operasyonel Verimlilik ve Yöntemsel Çeşitlilik

Taha Talip Türkistanlı(MERSİN ÜNİVERSİTESİ, DENİZCİLİK FAKÜLTESİ)0000-0003-4903-6138
Pages
160–191
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

For emerging economies like Türkiye, adopting such a layered and standardized approach offers a path to greater competitiveness and resilience in the global maritime sector.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Container port and terminal performance is central to the efficiency and resilience of global maritime logistics.
📊
Key Findings & Data:This study conducts a systematic review of methodologies used to evaluate container port performance, with a focus on both international practices and applications within Türkiye.
💡
Conclusion & Impact:For emerging economies like Türkiye, adopting such a layered and standardized approach offers a path to greater competitiveness and resilience in the global maritime sector.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:etkinlik analizi · simülasyon ve optimizasyon · veri odaklı yaklaşımlar

Abstract

Container port and terminal performance is central to the efficiency and resilience of global maritime logistics. This study conducts a systematic review of methodologies used to evaluate container port performance, with a focus on both international practices and applications within Türkiye. The review identifies five methodological families: frontier-based efficiency analysis, simulation and optimization models, statistical regression models, KPI and benchmarking frameworks, and hybrid AI-enhanced methods. A consistent set of core operational metrics appears across all approaches, including vessel turnaround time, berth or crane throughput, and container dwell time. These metrics serve as foundational indicators in performance assessment. However, methodological diversity and context-specific definitions limit cross-port comparability. Turkish case studies demonstrate the integration of MCDM, fuzzy logic, and machine learning to address local efficiency and sustainability challenges. The review proposes a tiered framework that aligns methods with operational, strategic, and technological goals. It recommends a shift from isolated methods to integrated, data-driven systems that support real-time decision making and long-term planning. For emerging economies like Türkiye, adopting such a layered and standardized approach offers a path to greater competitiveness and resilience in the global maritime sector.

Özet

Konteyner limanı ve terminal performansı, küresel denizcilik lojistiğinin verimliliği ve dayanıklılığı açısından belirleyici bir rol oynamaktadır. Bu çalışma, konteyner limanı performansını değerlendirmede kullanılan yöntemleri sistematik biçimde incelemekte; uluslararası yaklaşımlar ile Türkiye’deki uygulamalara odaklanmaktadır. İnceleme, beş ana yöntem ailesini belirlemiştir: sınır temelli etkinlik analizleri, benzetim ve optimizasyon modelleri, istatistiksel regresyon çerçeveleri, KPI ve kıyaslama sistemleri ile yapay zekâ destekli hibrit yöntemler. Tüm bu yaklaşımlar arasında gemi dönüş süresi, rıhtım veya vinç verimliliği ve konteyner bekleme süresi gibi temel operasyonel göstergeler ortak olarak öne çıkmaktadır. Ancak yöntemsel çeşitlilik ve bağlama özgü tanımlar, limanlar arası karşılaştırılabilirliği sınırlamaktadır. Türk örnek olayları, etkinlik ve sürdürülebilirlik sorunlarını ele almak üzere çok ölçütlü karar verme (MCDM), bulanık mantık ve makine öğrenmesi tekniklerinin giderek daha fazla bütünleştirildiğini göstermektedir. Çalışma, analitik yöntemleri operasyonel, stratejik ve teknolojik hedeflerle ilişkilendiren katmanlı bir çerçeve önermekte; tekil modellerden bütünleşik, veri odaklı sistemlere geçişi savunmaktadır. Türkiye gibi gelişmekte olan ekonomiler için bu tür standartlaştırılmış ve katmanlı bir yaklaşım, küresel denizcilik sektöründe daha yüksek rekabetçilik ve dayanıklılık kazandırma potansiyeli taşımaktadır.

🔖

Cite This Article

Türkistanlı, T. T. (2025). Operational Efficiency and Methodological Diversity in Container Port Performance Assessment. Mersin Üniversitesi Denizcilik ve Lojistik Araştırmaları Dergisi, Cilt: 7 Sayı: 2, 160–191. https://doi.org/10.54410/denlojad.1800788

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (96)

  1. Abourraja, M. N., Oudani, M., Samiri, M. Y., Boudebous, D., Fazziki, A. El, Najib, M., Bouain, A., & Rouky, N. (2017). A Multi-Agent Based Simulation Model for Rail–Rail Transshipment: An Engineering Approach for Gantry Crane Scheduling. IEEE Access, 5, 13142–13156. DOI: 10.1109/ACCESS.2017.2713246
  2. Abril, D., Paternina-Arboleda, C. D., & Velasquez-Bermudez, J. (2024). An Integrated Event-Driven Real-Time Tactical–Operational Optimization Framework for Smart Port Operations Planning. Logistics, 8(3), 65. https://doi.org/10.3390/logistics8030065
  3. Acer, A., & Yanginlar, G. (2017). The determination of Turkish container ports performance with TOPSIS multiple criteria decision making method. Journal of Management Marketing and Logistics, 4(2), 67–75. DOI:10.17261/Pressacademia.2017.452
  4. Albayrak, Ö. K. (2025). Performance Evaluation of Turkish Ports: Integrated Fuzzy Entropy-Fuzzy MARCOS Analysis. Verimlilik Dergisi, PRODUCTIVITY FOR LOGISTICS, 149–166. DOI: 10.51551/verimlilik.1532908
  5. Angeloudis, P., & Bell, M. G. H. (2011). A review of container terminal simulation models. Maritime Policy & Management, 38(5), 523–540. DOI: 10.1080/03088839.2011.597448
  6. Ansorena, I. L., & Valdecantos, V. N. (2021). A simulation model of container terminals. The Port of Valencia case study. International Journal of Industrial and Systems Engineering, 37(1), 15. https://doi.org/10.1504/IJISE.2021.112473
  7. Aslam, S., Michaelides, M. P., & Herodotou, H. (2024). A survey on computational intelligence approaches for intelligent marine terminal operations. IET Intelligent Transport Systems, 18(5), 755–793. https://doi.org/10.1049/itr2.12469
  8. Basarici, A. S., & Satır, T. (2019). Empty Container Movements Arising From Cargo Seasonality: Turkish Terminals. Maritime Business Review, 4(3), 238–255. https://doi.org/10.1108/mabr-03-2019-0011
  9. Baştuğ, S. (2023). Port Efficiency Evaluation of Turkish Container Ports Based on DEA-SCOR Model: An Effective Sea Gateways in Türkiye for One Belt and One Road Initiative. Marine Science and Technology Bulletin, 12(1), 27–38. https://doi.org/10.33714/masteb.1211636
  10. Bergantino, A. S., Musso, E., & Porcelli, F. (2013). Port management performance and contextual variables: Which relationship? Methodological and empirical issues. Research in Transportation Business & Management, 8, 39–49. https://doi.org/10.1016/j.rtbm.2013.07.002
  11. Bett, D. K., Ali, I., Gheith, M., & Eltawil, A. (2024). Simulation-Based Optimization of Truck Appointment Systems in Container Terminals: A Dual Transactions Approach with Improved Congestion Factor Representation. Logistics, 8(3), 80. https://doi.org/10.3390/logistics8030080
  12. Bichou, K. (2013). An empirical study of the impacts of operating and market conditions on container-port efficiency and benchmarking. Research in Transportation Economics, 42(1), 28–37. https://doi.org/10.1016/j.retrec.2012.11.009
  13. Braimoh, K. (2021). Applying the Balanced Scorecard (BSC) Approach to Improve Port Performance. International Journal of Multidisciplinary and Scientific Emerging Research, 09(04). https://doi.org/10.15662/IJMSERH.2021.0904002
  14. Bui, T. U., & Cho, G. S. (2025). Efficiency analysis of container ports in vietnam using stochastic frontier analysis. Journal of Ocean Engineering and Technology, 39(1), 56–62.
  15. Carboni, A., Deflorio, F., Caballini, C., & Cangelosi, S. (2025). Advances in terminal management: simulation of vehicle traffic in container terminals. Maritime Economics & Logistics, 27(3), 500–524. https://doi.org/10.1057/s41278-024-00300-5
  16. Carvalho da Silva, J., & Ensslin, S. (2024, June). Performance evaluation in the port sector: A systematic literature review. Maritime Transport Conference. https://doi.org/10.5821/mt.12830
  17. Chen, L., Zhang, D., Ma, X., Wang, L., Li, S., Wu, Z., & Pan, G. (2016). Container Port Performance Measurement and Comparison Leveraging Ship GPS Traces and Maritime Open Data. IEEE Transactions on Intelligent Transportation Systems, 17(5), 1227–1242. https://doi.org/10.1109/tits.2015.2498409
  18. Chen, Y., Yang, D., Lian, P., Wan, Z., & Yang, Y. (2020). Will structure-environment-fit result in better port performance? —An empirical test on the validity of Matching Framework Theory. Transport Policy, 86, 23–33. https://doi.org/10.1016/j.tranpol.2019.12.003
  19. Chhetri, P. K., Jayatilleke, G. B., Gekara, V. O., Manzoni, A., & Corbitt, B. J. (2016). Container terminal operations simulator (CTOS) – Simulating the impact of extreme weather events on port operation. European Journal of Transport and Infrastructure Research.
  20. Clark, X., Dollar, D., & Micco, A. (2004). Port efficiency, maritime transport costs, and bilateral trade. Journal of Development Economics, 75(2), 417–450. https://doi.org/10.1016/j.jdeveco.2004.06.005
  21. Cullinane, K., & Song, D.-W. (2006). Estimating the relative efficiency of European container ports: a stochastic frontier analysis. Research in Transportation Economics, 16, 85–115.
  22. Cullinane, K., Wang, T.-F., Song, D.-W., & Ji, P. (2006). The technical efficiency of container ports: Comparing data envelopment analysis and stochastic frontier analysis. Transportation Research Part A: Policy and Practice, 40(4), 354–374.
  23. Danladi, C., Tuck, S., Tziogkidis, P., Tang, L., & Okorie, C. (2024). Efficiency analysis and benchmarking of container ports operating in lower-middle-income countries: a DEA approach. Journal of Shipping and Trade, 9(1), 7.
  24. Ding, Y., Zhang, Z., Chen, K., Ding, H., Voss, S., Heilig, L., Chen, Y., & Chen, X. (2023). Real-Time Monitoring and Optimal Resource Allocation for Automated Container Terminals: A Digital Twin Application at the Yangshan Port. Journal of Advanced Transportation, 2023, 1–12. https://doi.org/10.1155/2023/6909801
  25. Divandri, A., & Yousefi, H. (2011). Balanced Scorecard: A Tool for Measuring Competitive Advantage of Ports with Focus on Container Terminals. International Journal of Trade, Economics and Finance, 472–477. https://doi.org/10.7763/IJTEF.2011.V2.151
  26. Dragović, B., Tzannatos, E., & Park, N. K. (2017). Simulation modelling in ports and container terminals: literature overview and analysis by research field, application area and tool. Flexible Services and Manufacturing Journal, 29(1), 4–34. https://doi.org/10.1007/s10696-016-9239-5
  27. Du, D., Liu, T., & Guo, C. (2023). Analysis of Container Terminal Handling System Based on Petri Net and ExtendSim. Promet-Traffic&Transportation, 35(1), 87–105.
  28. Ducruet, C., Itoh, H., & Merk, O. (2014). Time efficiency at world container ports.
  29. Elgazzar, S. H., & Ismail, A. (2021). Enhancing Egyptian container terminals performance through managing efficiency and competitiveness. Marine Economics and Management, ahead-of-print.
  30. Ensslin, S. R., Dutra, A., & Rambo, M. A. (2024). Performance evaluation from the infrastructure perspective in ports and container terminals. Maritime Transport Conference, 10.
  31. Fahim, P. B. M., Rezaei, J., Montreuil, B., & Tavasszy, L. (2022). Port performance evaluation and selection in the Physical Internet. Transport Policy, 124, 83–94. https://doi.org/10.1016/j.tranpol.2021.07.013
  32. Fernandes, A., Gutierres, D., Fugihara, M., & De Norman, B. (2024). Port Management Digital Twin and Control Tower Integration: An Approach to Support Real-Time Decision Making. 2024 Winter Simulation Conference (WSC), 2821–2831.
  33. Fri, M., Douaioui, K., Tetouani, S., Mabrouki, C., & Semma, E. A. (2020). A DEA-ANN framework based in Improved Grey Wolf Algorithm to evaluate the performance of container terminal. IOP Conference Series: Materials Science and Engineering, 827.
  34. Gharehgozli, A. H., Roy, D., & de Koster, R. (2016). Sea container terminals: New technologies and OR models. Maritime Economics & Logistics, 18(2), 103–140. https://doi.org/10.1057/mel.2015.3
  35. Gningue, M., Bedoui, W., & Venkatesh, V. G. (2024). A port performance measurement approach using a sustainability balanced scorecard based on stakeholders’ expectations. Maritime Policy & Management, 51(8), 1861–1883.
  36. Gökkuş, Ü., Yıldırım, M. S., & Aydin, M. M. (2017). Estimation of container traffic at seaports by using several soft computing methods: a case of Turkish Seaports. Discrete Dynamics in Nature and Society, 2017(1), 2984853.
  37. Görçün, Ö. F. (2021). Efficiency analysis of Black sea container seaports: application of an integrated MCDM approach. Maritime Policy & Management, 48(5), 672–699.
  38. Guruviswas, J. B. V, Sarkar, S., Gupta, N., Machavarapu, P. K., & Sridharan, N. (2025). Enhancing port performance assessment through new performance indicators and MCDM techniques. Transportation Research Procedia, 90, 655–662.
  39. Ha, M. (2017). MEASUREMENT, MODELLING AND ANALYSIS OF CONTAINER PORT PERFORMANCE. Liverpool John Moores University. https://doi.org/10.24377/LJMU.T.00005394
  40. Hamid, N. (2018). Factor analysis for balanced scorecard as measuring competitive advantage of infrastructure assets of owned state ports in Indonesia: Pelindo IV, Makassar, Indonesia. International Journal of Law and Management, 60(2), 386–401.
  41. Heebkhoksung, K. (2024). The Model of Sustainability Balanced Scorecard and Supply Chain in Port Management for Tourism. Economies, 12(5), 123. https://doi.org/10.3390/economies12050123
  42. Hlali, A. (2018). Efficiency Analysis with Different Models: The Case of Container Ports. Journal of Marine Science: Research & Development, 8, 1–10.
  43. Hsu, H.-P., Wang, C.-N., Thanh Tam Nguyen, T., Dang, T.-T., & Pan, Y.-J. (2024). Hybridizing WOA with PSO for coordinating material handling equipment in an automated container terminal considering energy consumption. Advanced Engineering Informatics, 60, 102410. https://doi.org/10.1016/j.aei.2024.102410
  44. Jafari, H. (2013). Presenting an integrative approach of mappac and fanp and balanced scorecard for performance measurements of container terminals. International Journal of Basic Sciences & Applied Research, 2(4), 494–504.
  45. Jobran, Y., & Kara, G. (2022). Examining the efficiency of automation in Container terminals. Journal of Transportation and Logistics, 7(1), 137–155.
  46. Jonker, T., Duinkerken, M. B., Yorke-Smith, N., de Waal, A., & Negenborn, R. R. (2021). Coordinated optimization of equipment operations in a container terminal. Flexible Services and Manufacturing Journal, 33(2), 281–311.
  47. Jung, B. (2011). Economic Contribution of Ports to the Local Economies in Korea. The Asian Journal of Shipping and Logistics, 27(1), 1–30. https://doi.org/10.1016/S2092-5212(11)80001-5
  48. Kastner, M., Nellen, N., Schwientek, A., & Jahn, C. (2021). Integrated Simulation-Based Optimization of Operational Decisions at Container Terminals. Algorithms, 14(2), 42. https://doi.org/10.3390/a14020042
  49. Kishore, L., Pai, Y. P., Ghosh, B. K., & Pakkan, S. (2024). Maritime shipping ports performance: a systematic literature review. Discover Sustainability, 5(1), 108. https://doi.org/10.1007/s43621-024-00299-y
  50. Kizilay, D., & Eliiyi, D. T. (2021). A comprehensive review of quay crane scheduling, yard operations and integrations thereof in container terminals. Flexible Services and Manufacturing Journal, 33(1), 1–42.
  51. Kotachi, M., Rabadi, G., & Obeid, M. F. (2013). Simulation Modeling and Analysis of Complex Port Operations with Multimodal Transportation. Procedia Computer Science, 20, 229–234. https://doi.org/10.1016/j.procs.2013.09.266
  52. Kurniawan, F., Musa, S. N., Moin, N. H., & Sahroni, T. R. (2022). A Systematic Review on Factors Influencing Container Terminal’s Performance. Operations and Supply Chain Management: An International Journal, 174–192. https://doi.org/10.31387/oscm0490339
  53. Li, B., & Song, G. (2020). Computational Logistics for Container Terminal Logistics Hubs Based on Computational Lens and Computing Principles. IEEE Access, 8, 194820–194835. https://doi.org/10.1109/ACCESS.2020.3033849
  54. Min, H., & Park, B.-I. (2008). A hybrid Data Envelopment Analysis and simulation methodology for measuring capacity utilisation and throughput efficiency of container terminals. International Journal of Logistics Systems and Management, 4(6), 650–672.
  55. Morales-Fusco, P., Saurí, S., Lekka, A. M., & Karousos, I. (2016). Assessing customs performance in the Mediterranean ports. KPI selection and Best practices identification as part of the MEDNET project. Transportation Research Procedia, 18, 374–383.
  56. Munim, Z. H., & Schramm, H.-J. (2018). The impacts of port infrastructure and logistics performance on economic growth: the mediating role of seaborne trade. Journal of Shipping and Trade, 3(1), 1. https://doi.org/10.1186/s41072-018-0027-0
  57. Musso, E., & Sciomachen, A. (2020). Impact of megaships on the performance of port container terminals. Maritime Economics & Logistics, 22(3), 432–445. https://doi.org/10.1057/s41278-019-00120-y
  58. Nations, U. (2020). Review of Maritime Transport 2019. United Nations Fund for Population Activities. https://books.google.com.tr/books?id=LOwwzQEACAAJ
  59. Ngangaji, M. M. F. (2019). An assessment of container terminal efficiency in East Africa ports using data envelopment analysis (DEA): the case of Dar es Salaam & Mombasa ports.
  60. Nikolaou, I. E., & Tsalis, T. A. (2013). Development of a sustainable balanced scorecard framework. Ecological Indicators, 34, 76–86.
  61. Nikolaou, P., & Dimitriou, L. (2021). Lessons to be learned from top-50 global container port terminals efficiencies: A multi-period DEA-tobit approach. Maritime Transport Research, 2, 100032.
  62. Notteboom, T., Coeck, C., & Broeck, J. Van Den. (2000). Measuring and Explaining the Relative Efficiency of Container Terminals by Means of Bayesian Stochastic Frontier Models. International Journal of Maritime Economics, 2(2), 83–106. https://doi.org/10.1057/ijme.2000.9
  63. Notteboom, T., & Rodrigue, J.-P. (2022). Maritime container terminal infrastructure, network corporatization, and global terminal operators: Implications for international business policy. Journal of International Business Policy, 6(1), 67.
  64. Park, J., & Sung, S.-I. (2016). Integrated Approach to Construction of Benchmarking Network in DEA-Based Stepwise Benchmark Target Selection. Sustainability, 8, 600.
  65. Parmaksizoglou, I. A., Bombelli, A., & Sharpanskykh, A. (2025). A Multi-Agent Optimization Approach for Multimodal Collaboration in Marine Terminals. Logistics, 9(3), 110. https://doi.org/10.3390/logistics9030110
  66. Pinto, M. M. O., Goldberg, D. J. K., & Cardoso, J. S. L. (2017). Benchmarking operational efficiency of port terminals using the OEE indicator. Maritime Economics & Logistics, 19(3), 504–517. https://doi.org/10.1057/mel.2016.6
  67. Rahman, M. M., Jahin, M. A., Islam, M. S., & Mridha, M. F. (2024). Optimizing container loading and unloading through dual-cycling and dockyard rehandle reduction using a hybrid genetic algorithm.
  68. Rødseth, K. L., Kuosmanen, T., & Holmen, R. B. (2025). Mitigating simultaneity bias in seaport efficiency measurement. Transportation Research Part A: Policy and Practice, 192, 104333. https://doi.org/10.1016/j.tra.2024.104333
  69. Roy, D., & de Koster, M. B. M. (2014). Modeling and design of container terminal operations. ERIM Report Series Reference No. ERS-2014-008-LIS.
  70. Sarwar, N. (2013). Time-related key performance indicators and port performance: a review of theory and practice.
  71. Satan, E., Aydın, U., & Atak, Ü. (2025). Liman Performans Analizi İçin Yeni Entegre D-CRITIC-SWARA-COPRAS Yaklaşımı. Verimlilik Dergisi, 59(1), 61–76.
  72. Sergei, K., Anatoly, K., Andrei, B., Anastasia, K., & Elena, S. (2024). Optimization of Seaport Operations Using Decision Support Methods and Simulation Modeling. 2024 International Conference on Decision Aid Sciences and Applications (DASA), 1–6. https://doi.org/10.1109/DASA63652.2024.10836342
  73. Sharma, M. J., & Yu, S. J. (2010). Benchmark optimization and attribute identification for improvement of container terminals. European Journal of Operational Research, 201(2), 568–580. https://doi.org/10.1016/j.ejor.2009.03.021
  74. Shevchenko, A. M., & Dyda, A. A. (2025). Determination of optimal parameters for efficient terminal operation by means of a simulation model in the AnyLogic environment. Computational Nanotechnology, 12(2), 98–108. https://doi.org/10.33693/2313-223X-2025-12-2-98-108
  75. Sislian, L., & Jaegler, A. (2018). A sustainable maritime balanced scorecard applied to the Egyptian Port of Alexandria. Supply Chain Forum: An International Journal, 19(2), 101–110.
  76. Söylemez, E. Y. (2025). Assessment of Operational, Environmental and Social Performance of Container Ports in Türkiye. Verimlilik Dergisi.
  77. Stickler, C. (2025). Automation KPIs: The Metrics That Drive Efficient Container Terminals.
  78. Suárez‐Gargallo, C., & Zaragoza‐Sáez, P. (2023). Port authority of Cartagena: Evidence of a sustainability balanced scorecard. Sustainable Development, 31(5), 3761–3785.
  79. Tengecha, N. A., & Zhang, X. (2022). An Efficient Algorithm for the Berth and Quay Crane Assignments Considering Operator Performance in Container Terminal Using Particle Swarm Model. Journal of Marine Science and Engineering, 10(9), 1232. https://doi.org/10.3390/jmse10091232
  80. Tongzon, J. (2001). Efficiency measurement of selected Australian and other international ports using data envelopment analysis. Transportation Research Part A: Policy and Practice, 35(2), 107–122. https://doi.org/10.1016/S0965-8564(99)00049-X
  81. Tuncel, G., Yalçınkaya, Ö., Deniz, E., & Esmer, S. (2024). Simulation Modeling Frameworks for Single-Cycling and Double-Cycling Strategies in Container Terminals. Journal of ETA Maritime Science, 319–331. https://doi.org/10.4274/jems.2024.59862
  82. (UNCTAD), U. N. C. on T. and D. (2023). Review of Maritime Transport 2023: Towards a Green and Just Transition. UN. https://books.google.com.tr/books?id=3J_2EAAAQBAJ
  83. Vaggelas, G. K. (2019). Measurement of port performance from users’ perspective. Maritime Business Review, 4(2), 130–150. https://doi.org/10.1108/mabr-08-2018-0024
  84. Van Battum, C. H. H., Wiegmans, B., Atasoy, B., van Wingerden, E., de Waal, A., & Tavasszy, L. A. (2023). Performance improvements in container terminals through the bottleneck mitigation cycle. Maritime Economics & Logistics, 25(1), 174–195. https://doi.org/10.1057/s41278-022-00245-7
  85. Wang, C.-N., Nguyen, N.-A.-T., Fu, H.-P., Hsu, H.-P., & Dang, T.-T. (2021). Efficiency Assessment of Seaport Terminal Operators Using DEA Malmquist and Epsilon-Based Measure Models. Axioms, 10(2), 48. https://doi.org/10.3390/axioms10020048
  86. Wang, P., Li, J., & Cao, X. (2024). Discrete Dynamic Berth Allocation Optimization in Container Terminal Based on Deep Q-Network. Mathematics, 12(23), 3742. https://doi.org/10.3390/math12233742
  87. World Bank. (2025). The Container Port Performance Index 2020 to 2024 Trends and Lessens Learned.
  88. Wu, J., Liang, L., & Song, M. (2010). Performance Based Clustering for Benchmarking of Container Ports: An Application of Dea and Cluster Analysis Technique. International Journal of Computational Intelligence Systems, 3(6), 709–722. https://doi.org/10.1080/18756891.2010.9727734
  89. Wu, J., & Liang, N. A. (2009). Performances and benchmarks of container ports using data envelopment analysis. International Journal of Shipping and Transport Logistics, 1(3), 295. https://doi.org/10.1504/ijstl.2009.027536
  90. Yu, M., Gu, Q., & Zhang, L. (2025). Evaluating Operating Performance of New Automated Terminals: A Comparison from the Perspective of Storage Space Allocation. https://doi.org/10.3233/ATDE250493
  91. Yuhling, S. U., Liang, G.-S., Chin-Feng, L. I. U., & Tsung-Yu, C. (2003). A study on integrated port performance comparison based on the concept of balanced scorecard. Journal of the Eastern Asia Society for Transportation Studies, 5(1), 609–624.
  92. Zajac, M. (2024). Adaptive Performance Evaluation of Container Terminals Through Normalization and Parameter Analysis. Logistics, 9(1), 2.
  93. Zhang, J., Yang, D., & Luo, M. (2024). Port efficiency types and perspectives: A literature review. Transport Policy, 156, 13–24. https://doi.org/10.1016/j.tranpol.2024.07.014
  94. Zhang, X., Zeng, Q., & Sheu, J.-B. (2019). Modeling the productivity and stability of a terminal operation system with quay crane double cycling. Transportation Research Part E: Logistics and Transportation Review, 122, 181–197. https://doi.org/10.1016/j.tre.2018.12.003
  95. Zhou, L., & Lee, H. (2022). A Study on Port (Terminals) Operational Efficiency Analysis Using PCA-DEA Approach: Focusing on Container Terminals on Busan Port Lele Zhou, & Hyangsook Lee (2022-09-28). A Study on Port (Terminals) Proceedings of the KOR-KST Conference, 324–329.
  96. Zhou, L., & Suh, W. (2024). A Comprehensive Study on Static and Dynamic Operational Efficiency in Major Korean Container Terminals Amid the Smart Port Development Context. Sustainability, 16(13), 5288. https://doi.org/10.3390/su16135288