Suzen, Rasih
Promotor(s) : Dawidowicz, Ludmila ; Caprace, Jean-David
Date of defense : 2015 • Permalink : http://hdl.handle.net/2268.2/6175
Details
Title : | Discrete Event Simulation Helps to Improve Terminal Productivity for New Design Container Ships |
Author : | Suzen, Rasih |
Date of defense : | 2015 |
Advisor(s) : | Dawidowicz, Ludmila
Caprace, Jean-David |
Committee's member(s) : | Rigo, Philippe |
Language : | English |
Number of pages : | 90 |
Keywords : | [en] Container terminal operations, crane efficiency, discrete event simulation, stochastic approach |
Discipline(s) : | Engineering, computing & technology > Civil engineering |
Target public : | Researchers Professionals of domain Student |
Institution(s) : | Université de Liège, Liège, Belgique |
Degree: | Master de spécialisation en construction navale |
Faculty: | Master thesis of the Faculté des Sciences appliquées |
Abstract
[en] Efficiency improvement in container terminal operations can lead to increase service capacity, reduce berthing time and operational expenses of ports. Moreover, being faster in ports allows a ship to transit at lower speeds (slow steaming) thus to save fuel as well as to reduce emissions, or else ship can sail at same speed to have higher annual cargo capacity and income. Despite that there being researches about existing container terminal productivity assessment, no papers analyzing port efficiency of a new bay plan design of a container ship stochastically have been published. This thesis proposes a productivity analysis of a new bay plan design which intends to be faster during loading and unloading at container terminals. The operational efficiency of a container terminal is investigated for various conditions and its effect on berthing time is reviewed. Port productivity, i.e. the time needed to move a selected number of containers, is assessed using a Discrete Event Simulation methodology. A fully parametrical port simulation model is created and calibrated based on a 7 months statistical data set of a real container terminal. The uncertainties and unpredictable events i.e. several types of delays related to operations are implemented using semi-random numbers. Following the description of the stochastic parameters included in the model, the simulation is repeated until sufficiently large sets of iterations are available for statistical analysis. Then, the dispersion of results regarding the port productivity are discussed and compared to measured data. Finally, we obtain the results concerning efficiency of a new bay plan where various conditions, such as the high/low tide, higher/lower crane speeds and multiple crane usage, are considered We suggest that DES is one of the most precise analysis and decision assistance tool to accomplish operational performance studies for new bay plans and container terminals.
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