The impact of seasonal demand fluctuations on service network design of container feeder lines

Year 2016, Volume: 1 Issue: 1, 39 - 58, 29.04.2016

Olcay Polat , Hans-otto Günther

https://doi.org/10.22532/jtl.237886

Cited By: 4

Abstract

Customer demand in global supply networks is highly uncertain due to unexpected global and local economic conditions and, in addition, affected by seasonal demand fluctuations for final products. Therefore, in maritime transportation the design of short-sea shipping services for containerized goods has to prove its economic efficiency under varying conditions of transportation demand. Since liner shipping involves significant capital investments and huge daily operating costs, the appropriate design of the service network is crucial for the profitability of the container feeder lines. Usually, quantitative models for shipping network design are based on deterministic forecasts, which are prone to errors caused by uncertainty factors and structural changes in the development of demand. This paper puts special emphasis on the impact of seasonal demand fluctuations on the structure of the related H&S networks, the capacity of the fleet operating within the network, the deployment of ship types as well as on the associated routes of the ships. A simulation and artificial neural network based forecasting framework is developed to support the design of service networks of short-sea shipping lines. The proposed methodology has been tested for a feeder liner shipping service in the East Mediterranean and Black Sea region. Numerical results show that seasonal demand fluctuations have vital impact on the network design of container feeder lines.

Keywords

Feeder service network design, container shipping, forecasting, simulation, liner shipping, artificial neural network, seasonality

Mevsimsel Talep Dalgalanmalarının Besleyici Konteynır Hatlarının Servis Ağı Tasarımındaki Etkisi

Abstract

Küresel tedarik ağlarındaki müşteri talebi beklenmedik küresel ve yerel ekonomik krizlerden dolayı oldukça belirsiz olup son ürünlerdeki mevsimsel talep dalgalanmalarından etkilenmektedir. Bu nedenle konteynır yükleri için denizyolu taşımacılığı servis tasarımları, değişen nakliye talepleri altında ekonomik etkinliklerini ortaya koymak zorundadırlar. Düzenli hat deniz yolu taşımacılığı önemli bir sermaye yatırımı içerdiğinden  uygun servis ağı tasarımı besleyici konteynır hatlarının karlılığı için çok önemlidir. Genellikle denizyolu taşımacılığı ağ tasarımı için kullanılan sayısal modeller, belirsizlik faktörleri ve talebin gelişimindeki yapısal değişiklikler nedeni ile hatalara neden olabilen deterministik tahminlemelere dayanmaktadır. Bu çalışma mevsimsel talep dalgalarının ilgili servis ağlarının yapısındaki etkisi, ağ içerisinde operasyon gösteren filonun kapasitesi, gemi tiplerinin açılımıyla birlikte gemilerin ilişkilendikleri rotaların belirlenmesine de özel vurgu yapmaktadır. Çalışmada, denizyolu taşımacılığı servis ağlarının tasarlanmasına destek sağlamak için bir benzetim ve yapay sinir ağı temelli tahminleme yapısı besleyici tasarlanmıştır. Önerilen yöntem doğu Akdeniz ve Karadeniz havzasındaki bir besleyici denizyolu taşımacılığı servisi için test edilmiştir. Sayısal sonuçlar mevsimsel talep dalgalanmalarının besleyici hatların servis tasarımları üzerinde hayati öneme sahip olduğunu göstermektedir

Keywords

Besleyici servis ağı tasarımı, konteynır taşıma, tahminleme, benzetim, düzenli hat denizyolu taşımacılığı, yapay sinir ağları, mevsimsellik

References

• Andersen MW (2010) Service network nesign and management in liner container shipping applications (Chapter 5). Ph.D. Thesis, Technical University of Denmark
• 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
• Bose NK, Liang P (1996 ) Neural network fundamentals with graphs, algorithms, and applications. McGraw-Hill, Inc., Hightstown, NJ, USA
• Chen S-H, Chen J-N (2010) Forecasting container throughputs at ports using genetic programming. Expert Systems with Applications 37:2054-2058
• 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
• Christiansen M, Fagerholt K, Ronen D (2004) Ship routing and scheduling: Status and perspective. Transportation Science 38:1-18
• de Gooijer JG, Klein A (1989) Forecasting the Antwerp maritime steel traffic flow: a case study. Journal of Forecasting 8:381-398
• 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
• 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
• Dyamar (2009) Black Sea container trades, ports & terminals, hinterland. Dyamar B.V., Alkmaar
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• Huang A, Qiao H, Wang S (2014) Forecasting Container Throughputs with Domain Knowledge. Procedia Computer Science 31:648-655
• 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
• 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,
• -
• 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
• Kjeldsen KH (2011) Classification of ship routing and scheduling problems in liner shipping. INFOR: Information Systems and Operational Research 49:139-152
• 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
• 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
• 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
• 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,
• 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
• 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,
• Lun YHV, Lai K-H, Cheng TCE (2010) Shipping and Logistics Management Springer-Verlag, London
• 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.,
• -
• 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
• Mostafa MM (2004) Forecasting the Suez Canal traffic: a neural network analysis. Maritime Policy & Management 31:139-156
• Notteboom TE (2004) Container shipping and ports: An overview. Review of Network Economics 3:86-106
• OceanShippingConsultants (2011) South Europe and Mediterranean container markets to 2025. Haskoning UK Ltd, Surrey
• OOCL (2015a) Asia-Europe (Loop 1). www.oocl.com/eng/ourservices/serviceroutes/aet/Pages/default.aspx. Accessed May 06, 2015
• OOCL (2015b) Intra-Europe (SBXB). www.oocl.com/eng/ourservices/serviceroutes/iet/Pages/default.aspx. Accessed May 06, 2015
• Pallis AA, de Langen PW (2010) Seaports and the structural implications of the economic crisis. Research in Transportation Economics 27:10-18
• Peng W-Y, Chu C-W (2009) A comparison of univariate methods for forecasting container throughput volumes. Mathematical and Computer Modelling 50:1045-1057
• Polat O (2013) Designing Liner Shipping Feeder Service Networks in the New Era of Mega Containerships. Doctoral Thesis, Technische Universität Berlin
• 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
• 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
• 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
• 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
• 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
• Polat O, Uslu EE (2010) Seasonality in Foreign Trade Data of Turkey. Gaziantep University Journal of Social Sciences 9:407-423
• Ronen D (1983) Cargo ship routing and scheduling: Survey of models and problems. European Journal of Operational Research 12:119-126
• Ronen D (1993) Ship scheduling: The last decade. European Journal of Operational Research 71:325-333
• 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),
• Schulze PM, Prinz A (2009) Forecasting container transshipment in Germany. Applied Economics 41:2809-2815
• 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
• Sun L (2010) The research on a double forecasting model of port cargo throughput. World Journal of Modeling and Simulation 6:57-62
• Tao L, Wang Y (2015) Port Container Throughput Forecasting Based on the Multiplicative Seasonal ARIMA Model. Operations Research and Fuzziology 5:30-37
• 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
• 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
• Walter M, Younger W (1988) Forecasting the demand for services of a new port. GeoJournal 16:295-300
• 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
• 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.,
• 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
• 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
• 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
• 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
• 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
• 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
• Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting 14:35-62
• 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,
• 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
• 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
• Zurada JM (1992) An Introduction to artificial Neural systems. West Publishing, St. Paul