Referanslar

• Andersen MW (2010) Service network nesign and management in liner container shipping applications (Chapter 5). Ph.D. Thesis, Technical University of Denmark google scholar
• Anqiang H, Zhenji Z, Xianliang S, Guowei H Forecasting container throughput with big data using a partially combined framework. In: Transportation Information and Safety (ICTIS), 2015 International Conference on, 25-28 June 2015 2015. pp 641-646. doi:10.1109/ICTIS.2015.7232102 google scholar
• Bose NK, Liang P (1996 ) Neural network fundamentals with graphs, algorithms, and applications. McGraw-Hill, Inc., Hightstown, NJ, USA google scholar
• Chen S-H, Chen J-N (2010) Forecasting container throughputs at ports using genetic programming. Expert Systems with Applications 37:2054-2058 google scholar
• Christiansen M, Fagerholt K, Nygreen B, Ronen D (2013) Ship routing and scheduling in the new millennium. European Journal of Operational Research 228:467–483 google scholar
• Christiansen M, Fagerholt K, Ronen D (2004) Ship routing and scheduling: Status and perspective. Transportation Science 38:1-18 google scholar
• de Gooijer JG, Klein A (1989) Forecasting the Antwerp maritime steel traffic flow: a case study. Journal of Forecasting 8:381-398 google scholar
• Ducruet C, Notteboom T (2012a) Developing liner service networks in container shipping. In: Song DW, Panayides P (eds) Maritime Logistics: A complete guide to effective shipping and port management. Kogan Page, Londan, pp 77-110 google scholar
• Ducruet C, Notteboom T (2012b) The worldwide maritime network of container shipping: spatial structure and regional dynamics. Global Networks 12:395-423 doi:10.1111/j.1471-0374.2011.00355.x google scholar
• Dyamar (2009) Black Sea container trades, ports & terminals, hinterland. Dyamar B.V., Alkmaar google scholar
• Fung MK (2001) Competition between the ports of Hong Kong and Singapore: a structural vector error correction model to forecast the demand for container handling services. Maritime Policy & Management 28:3- 22 google scholar
• Gao Y, Luo M, Zou G (2016) Forecasting with model selection or model averaging: a case study for monthly container port throughput. Transportmetrica A: Transport Science:1-19 doi:10.1080/23249935.2015.1137652 google scholar
• Gosasang V, Chandraprakaikul W, Kiattisin S (2011) A comparison of traditional and neural networks forecasting techniques for container throughput at Bangkok port. The Asian Journal of Shipping and Logistics 27:463-482 google scholar
• Goulielmos AM, Kaselimi E (2011) A non-linear forecasting of container traffic: the case-study of the Port of Piraeus, 1973-2008. International Journal of Shipping and Transport Logistics 3:72 - 99 google scholar
• Guo Z, Song X, Ye J (2005) A verhulst model on time series error corrected for port throughput forecasting. Journal of the Eastern Asia Society for Transportation Studies 6:881 - 891 google scholar
• Hoff A, Andersson H, Christiansen M, Hasle G, Løkettangen A (2010) Industrial aspects and literature survey: Fleet composition and routing. Computers & Opertions Research 37:2041-2061 google scholar
• Huang A, Lai K, Li Y, Wang S (2015a) Forecasting container throughput of Qingdao port with a hybrid model. J Syst Sci Complex 28:105-121 doi:10.1007/s11424-014-3188-4 google scholar
• Huang A, Lai KK, Qiao H, Wang S, Zhang Z (2015b) An Interval Knowledge Based Forecasting Paradigm for Container Throughput Prediction. Procedia Computer Science 55:1381-1389 doi:http://dx.doi.org/10.1016/j.procs.2015.07.126 google scholar
• Huang A, Qiao H, Wang S (2014) Forecasting Container Throughputs with Domain Knowledge. Procedia Computer Science 31:648-655 google scholar
• Hui ECM, Seabrooke W, Wong GKC (2004) Forecasting cargo throughput for the Port of Hong Kong: Error correction model approach. Journal of Urban Planning and Development 130:195-203 google scholar
• Hwang H-S, Bae S-T, Cho G-S (2007) Container terminal demand forecasting framework using fuzzy-GMDH and neural network method. Paper presented at the International Conference on Innovative Computing, Informatio and Control, Kumamoto, Japan, google scholar
• - google scholar
• Khashei M, Bijari M, Ardali GAR (2009) Improvement of auto-regressive integrated moving average models using fuzzy logic and artificial neural networks (ANNs). Neurocomputing 72:956- 967 google scholar
• Kjeldsen KH (2011) Classification of ship routing and scheduling problems in liner shipping. INFOR: Information Systems and Operational Research 49:139-152 google scholar
• Kline DM, Zhang GP (2004) Methods for multi-step time series forecasting with neural networks. In: Zhang GP (ed) Neural Networks for Business Forecasting. Information Science Publishing, pp 226-250 google scholar
• Kulak O, Polat O, Gujjula R, Günther H-O (2013) Strategies for improving a long-established terminal’s performance: a simulation study of a Turkish container terminal. Flexible Services and Manufacturing Journal 25:503-527 google scholar
• Lam WHK, Ng PLP, Seabrooke W, Hui CM (2004) Forecasts and reliability analysis of port cargo throughput in Hong Kong. Urban Planning & Development Journal 130:133-151 google scholar
• Li X, Xu S (2011) A study on port container throughput prediction based on optimal combined forecasting model in Shanghai port. Paper presented at the 11th International Conference of Chinese Transportation Professionals, Nanjing, China, google scholar
• Liu Z, Ji L, Ye Y, Geng Z (2007) Combined forecast method of port container throughput based on RBF neural network. Journal of Tongji University (Nature Science) 35:739-744 google scholar
• Løfstedt B, Alvarez JF, Plum CEM, Pisinger D, Sigurd MM (2010) An integer programming model and benchmark suite for liner shipping network design. vol Report 19.2010. DTU Management Engineering, google scholar
• Lun YHV, Lai K-H, Cheng TCE (2010) Shipping and Logistics Management Springer-Verlag, London google scholar
• Mak KL, Yang DH (2007) Forecasting Hong Kong’s container throughput with approximate least squares support vector. Paper presented at the Proceedings of the World Congress on Engineering, London, U.K., google scholar
• - google scholar
• Meng Q, Wang T, Wang S (2012) Short-term liner ship fleet planning with container transshipment and uncertain container shipment demand. European Journal of Operational Research 223:96-105 google scholar
• Mostafa MM (2004) Forecasting the Suez Canal traffic: a neural network analysis. Maritime Policy & Management 31:139-156 google scholar
• Notteboom TE (2004) Container shipping and ports: An overview. Review of Network Economics 3:86-106 google scholar
• OceanShippingConsultants (2011) South Europe and Mediterranean container markets to 2025. Haskoning UK Ltd, Surrey google scholar
• OOCL (2015a) Asia-Europe (Loop 1). www.oocl.com/eng/ourservices/serviceroutes/aet/Pages/default.aspx. Accessed May 06, 2015 google scholar
• OOCL (2015b) Intra-Europe (SBXB). www.oocl.com/eng/ourservices/serviceroutes/iet/Pages/default.aspx. Accessed May 06, 2015 google scholar
• Pallis AA, de Langen PW (2010) Seaports and the structural implications of the economic crisis. Research in Transportation Economics 27:10-18 google scholar
• Peng W-Y, Chu C-W (2009) A comparison of univariate methods for forecasting container throughput volumes. Mathematical and Computer Modelling 50:1045-1057 google scholar
• Polat O (2013) Designing Liner Shipping Feeder Service Networks in the New Era of Mega Containerships. Doctoral Thesis, Technische Universität Berlin google scholar
• Polat O, Günther H-O, Kulak O The containership feeder network design problem: the new Izmir port as hub in the black sea. In: The 2th International Conference on Logistics and Maritime Systems, Bremen, Germany, 22-24 August 2012a. pp 347-356 google scholar
• Polat O, Günther H-O, Kulak O (2014) The feeder network design problem: Application to container services in the Black Sea region. Maritime Economics & Logistics 16:343-369 doi:10.1057/mel.2014.2 google scholar
• Polat O, Kalayci CB, Kulak O, Günther H-O (2015) A perturbation based variable neighborhood search heuristic for solving the Vehicle Routing Problem with Simultaneous Pickup and Delivery with Time Limit. European Journal of Operational Research 242:369-382 doi:10.1016/j.ejor.2014.10.010 google scholar
• Polat O, Kulak O, Günther H-O (2011) A Monte Carlo simulation based stochastic forecasting frame: a case study of containerized trade of Turkish ports. Paper presented at the 12th International Symposium on Econometrics Operations Research and Statistics, Denizli, Turkey google scholar
• Polat O, Kulak O, Günther H-O An adaptive neighborhood search approach for VRPSPDTL. In: Günther H-O, Kim K-H, Kopfer H (eds) The 2nd International Conference on Logistics and Maritime Systems, Bremen, Germany, 22-24 August 2012b. pp 429-437 google scholar
• Polat O, Uslu EE (2010) Seasonality in Foreign Trade Data of Turkey. Gaziantep University Journal of Social Sciences 9:407-423 google scholar
• Ronen D (1983) Cargo ship routing and scheduling: Survey of models and problems. European Journal of Operational Research 12:119-126 google scholar
• Ronen D (1993) Ship scheduling: The last decade. European Journal of Operational Research 71:325-333 google scholar
• Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation, Vol. 1. In: Parallel distributed processing: explorations in the microstructure of cognition. MIT Press, Cambridge (MA), google scholar
• Schulze PM, Prinz A (2009) Forecasting container transshipment in Germany. Applied Economics 41:2809-2815 google scholar
• Seabrooke W, Hui ECM, Lam WHK, Wong GKC (2003) Forecasting cargo growth and regional role of the port of Hong Kong. Cities 20:51-64 google scholar
• Sun L (2010) The research on a double forecasting model of port cargo throughput. World Journal of Modeling and Simulation 6:57-62 google scholar
• Tao L, Wang Y (2015) Port Container Throughput Forecasting Based on the Multiplicative Seasonal ARIMA Model. Operations Research and Fuzziology 5:30-37 google scholar
• Tran N, Haasis H-D (2015) Literature survey of network optimization in container liner shipping. Flexible Services and Manufacturing Journal 27:139-179 doi:10.1007/s10696-013-9179-2 google scholar
• Varbanova A (2011) Current issues in operational planning of general cargo transportation on container feeder lines in the Black Sea region. The international virtual journal for science, techniques and innovations for the industry "Machines, Technologies, Materials” 2011:35-38 google scholar
• Walter M, Younger W (1988) Forecasting the demand for services of a new port. GeoJournal 16:295-300 google scholar
• Wang S, Meng Q, Bell MGH (2013) Liner ship route capacity utilization estimation with a bounded polyhedral container shipment demand pattern. Transportation Research Part B: Methodological 47:57-76 google scholar
• Wu D, Pan X (2010) Container volume forecasting of Jiujiang port based on SVM and game theory. Paper presented at the International Conference on Intelligent Computation Technology and Automation, Hunan, China., google scholar
• Xiao J, Xiao Y, Fu J, Lai K (2014) A transfer forecasting model for container throughput guided by discrete PSO. J Syst Sci Complex 27:181-192 doi:10.1007/s11424-014-3296-1 google scholar
• Xiao Y, Xiao J, Wang S (2012) A hybrid forecasting model for non-stationary time series: An application to container throughput prediction. International Journal of Knowledge and Systems Science 3:1-16 google scholar
• Xie G, Wang S, Zhao Y, Lai KK (2013) Hybrid approaches based on LSSVR model for container throughput forecasting: A comparative study. Applied Soft Computing 13:2232-2241 google scholar
• Yang Z, Chen K, Notteboom T (2012) Optimal design of container liner services: Interactions with the transport demand in ports. Maritime Economics & Logistics 14:409-434 google scholar
• Zachcial M, Lemper B (2006) Container shipping: An overview of Development Trends. In: Heideloff C, Pawlik T (eds) Handbook of Container Shipping Management. vol 32. Institute of Shipping Economics and Logistics (ISL), Bremen, Germany, pp 23-37 google scholar
• Zha X, Chai Y, Witlox F, Ma L (2016) Container Throughput Time Series Forecasting Using a Hybrid Approach. In: Jia Y, Du J, Li H, Zhang W (eds) Proceedings of the 2015 Chinese Intelligent Systems Conference: Volume 1. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 639-650. doi:10.1007/978-3-662-48386-2_65 google scholar
• Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting 14:35-62 google scholar
• Zhang P, Cui Y (2011) Research on combination forecast of port container throughput based on Elman neural network. Paper presented at the 3rd International Conference on Communication Software and Networks, Xi'an, China, google scholar
• Zheng J, Meng Q, Sun Z (2015) Liner hub-and-spoke shipping network design. Transportation Research Part E: Logistics and Transportation Review 75:32-48 google scholar
• Zohil J, Prijon M (1999) The MED rule: the interdependence of container throughput and transhipment volumes in the Mediterranean ports. Maritime Policy & Management 26:175-193 google scholar
• Zurada JM (1992) An Introduction to artificial Neural systems. West Publishing, St. Paul google scholar

Atıflar