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Evaluating the impact of long cargo dwell time on port performance: an evaluation model of Douala International Terminal in Cameroon

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Języki publikacji
EN
Abstrakty
EN
Create as part of the concession agreement signed by the Container Terminal 28 June 2004 with the port of Douala international terminal (DIT) Company aims to manage, operate and develop the Port’s container handling activity in Douala. This paper investigates the main factors explaining long container dwell times in Douala Port. Using original and extensive data on container imports in the Port of Douala, it seeks to provide a basic understanding of why containers stay on average more than two weeks in port space while long dwell times are widely recognized as a critical hindrance to economic development. It also demonstrates the interrelationships that exist between logistics performance of consignees, operational performance of port operators and efficiency of customs clearance operations. Shipment level analysis is used to identify the main determinants of long cargo dwell times and the impact of shipment characteristics such as fiscal regime, density of value, bulking and packaging type, last port of call, and region of origin or commodity group on cargo dwell time in ports is tested. External factors, such as performance of clearing and forwarding agents, shippers and shipping line strategies, also play an important role in the determination of long dwell times.
Rocznik
Strony
7--20
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
autor
autor
  • Wuhan University of Technology, School of Transportation, Wuhan, China, styjq@whut.edu.cn
autor
  • Wuhan University of Technology, School of Transportation, Wuhan, China, stephen.okyere@yahoo.com
  • Kumasi Technical University, Department of Procurement and SCM, Kumasi, Ghana
Bibliografia
  • [1] AGRESTI, A., 2002. Categorical Data Analysis. 2nd ed. New York: John Wiley & Sons.
  • [2] CHU, C. Y., & HUANG, W. C., 2005. Determining container terminal capacity on the basis of an adopted yard handling system. Transport Reviews, 25(2), 181-199.
  • [3] DALLY, H. K., 1983. Container Handling and Transport: A Manual of Current Practice.
  • [4] FOLTIN, P., GONTARCZYK, M., ŚWIDERSKI, A., & ZELKOWSKI, J., 2015. Evaluation model of the companies operating within lo-gistic network. Archives of Transport, 36(4), 21-33.
  • [5] GIULIANO, G., & O’BRIEN, T., 2007. Reducing port-related truck emissions: The terminal gate appointment system at the Ports of Los An-geles and Long Beach. Transportation Research Part D: Transport and Environment, 12(7), 460-473.
  • [6] HOFFMAN, P., 1985. Container facility planning: A case description. Port management text-book containerization. Institute of shipping economics and logistics.
  • [7] HOSMER, D. W., LEMESHOW, S., & MAY, S. 2008. Applied survival analysis: Regression Modeling of Time to Event Data. Wiley Blackwell.
  • [8] HOSMER, D.W. AND LEMESHOW, S., 2002. Applied Logistic Regression.. 2nd ed. New York: John Wiley & Sons,.
  • [9] HUANG, S. Y., HSU, W. J., CHEN, C., YE, R., & NAUTIYAL, S. 2008. Capacity analysis of container terminals using simulation techniques. International Journal of Computer Applications in Technology, 32(4), 246-253.
  • [10] JACYNA-GOLDA, I., 2015. Decision-making model for supporting supply chain efficiency evaluation. Archives of Transport , 33(1), 17-31.
  • [11] KGARE, T., RABALLAND, G., & ITTMANN, H. W., 2011. Cargo dwell time in Durban: Lessons for sub-Saharan African ports. The World Bank.
  • [12] KOUROUNIOTI, I., POLYDOROPOULOU, A., TSIKLIDIS, C., 2015. Development of a methodological framework for the dwell time of containers in Marine Terminals-First results, In proceeding ICTR.
  • [13] LITTLE, J. D. C., 1961. A Proof for the Queu-ing Formula: L = λW. Operations Research, June, Volume 9 Issue 3, p. 383–387.
  • [14] LONG, J. S., 1997. Regression Models of Categorical and Limited Dependent Variables. : Sage.
  • [15] MERCKX, 2005. The issue of dwell time charges to optimize container terminal capacity, Cyprus, Limassol.
  • [16] MOINI N., BOILE M., THEOFANIS S. AND LEVENTHAL W., 2008. Estimating the deter-minant factors of container DTs at seaports, Maritime Economy and Logistics, 14, 162–177.
  • [17] OCEAN SHIPPING CONSULTANTS, 2008. Beyond the Bottlenecks: Ports in Sub-Saharan Africa, s. l.: World Bank and the the SSATP.
  • [18] OTTJES, J. A., VEEKE, H. P., DUINKERKEN, M. B., RIJSENBRIJ, J. C., & LODEWIJKS, G., 2007. Simulation of a multiterminal system for container handling. In Container terminals and cargo systems (pp. 15-36). Springer, Berlin, Heidelberg.
  • [19] RABALLAND, G., REFAS, S., BEURAN, M., & ISIK, G. 2012. Why does cargo spend weeks in sub-Saharan African ports? Lessons from six countries. The World Bank.
  • [20] RALLABAND, G., 2013. Why High Dwell Times in African Ports?. World Bank Paper.
  • [21] REFAS, S., & CANTENS, T., 2011. Why does cargo spend weeks in African ports? The case of Douala, Cameroon. The World Bank.
  • [22] RODRIGUE, J. P., & NOTTEBOOM, T., 2009. The terminalization of supply chains: reassessing the role of terminals in port/hinterland logistical relationships. Maritime Policy & Management, 36(2), 165-183.
  • [23] WORLD BANK, 2007. Port Reform Toolkit. Second Edition. ed. s.l.:s.n.
  • [24] ZHAO, W., & GOODCHILD, A. V. 2010. The impact of truck arrival information on container terminal rehandling. Transportation Research Part E: Logistics and Transportation Review, 46(3), 327-343.
Uwagi
PL
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bed2cd38-3aee-4c8e-9a55-5028c51511e1
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