Efficiency analysis and Ranking of Mediterranean container Ports and terminals
DC Field | Value | Language |
---|---|---|
dc.contributor.author | HERMOUCHETOUFIKSABRI | - |
dc.date.accessioned | 2017-02-22T02:20:11Z | - |
dc.date.available | 2017-02-22T02:20:11Z | - |
dc.date.issued | 2011 | - |
dc.date.submitted | 2011-10-10 | - |
dc.identifier.uri | http://kmou.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000002174249 | ko_KR |
dc.identifier.uri | http://repository.kmou.ac.kr/handle/2014.oak/8233 | - |
dc.description.abstract | The Mediterranean Sea is the ‘crossroads’ of European, Asian and African continents whose trade is growing with globalisation. And as a ‘maritime route’ nearly a third of world trade ‘passes’, from the mouth of the Suez Canal to the Straits of Gibraltar or the Bosporus, from the Atlantic to the Black Sea making the region as one of the world’s major trade routes in addition of the trade developed by the coastal countries situated around this landlocked sea. For long time transport in the region has been dominated by the North-West European ports but during the last decade there is a consistent progress in ports situated on the south and east shores of the Mediterranean basin, notably Morocco, with the Tangier Med container transshipment terminals project, and also in Egypt, which has recently started the expertise of the private sector in delivering new capacity and new efficiencies. This changing environment has consistently scrambled up ports hierarchy in the region. In this paper the efficiency and performance is evaluated for 32 seaports in the Mediterranean region using a non-parametric linear programming method, DEA (Data Envelopment Analysis) which evaluates relative efficiencies of a homogenous set of decision making units (DMUs) in the presence of multiple input and output factors. Studies on the region using DEA never included the new emerging ports and terminal thus the ultimate goal of the study is to re-estimate the competitive environment of port industry in the region including ports from all Mediterranean sub-regions to fully assess ports’ activity in the region. By analyzing the operational efficiency, revealing the causes of inefficient operations, and suggesting how to overcome the drawbacks. An additional analysis for ranking the container ports was conducted using the super-efficiency model. | - |
dc.description.tableofcontents | AbstractI Contents.II List of tables.IV List of figures.V Chapter I Introduction.. 1 1. Research Background .. 1 2. Research Objectives.. 4 3. Method. 5 4. Research Scope. 6 5. Reasons to Use DEA 7 6. Organization of Chapters.. 5 Chapter II Literature Review.. 9 1. Theoretical Frame Work.. 9 1.1. Efficiency Measurement 9 1.2. Alternative Technics.. 10 1.2.1. Original Least Squares. 10 1.2.2. Corrected Original Least Squares 10 1.2.3. Stochastic Frontier. 11 1.2.4. Data Envelopment Analysis 11 2. Review of Previous Researches about Seaports.. 12 Chapter III container ports and terminals. 11 1. Container Ports and Terminals in the Overall Shipping Market. 14 2. The Operation and Cost Structure of Sea Ports 14 3. Structure of Global Container Shipping . 17 4. Functions and Configuration of the Container Port/Terminal 19 4.1. Marine Side Interface. 19 4.2. Transfer System.. 19 4.3. Container Storage System.. 20 4.4. Land Side Interface. 21 5. Technical Change In Ports And Terminals.. 23 Chapter IV Port Industry in the Mediterranean Region 25 1. Introduction to the Mediterranean Ports Environment. 25 2. Maritime Traffic in the Mediterranean Sea. 27 2.1. Extra-Med Traffic 27 2.2. Intra-Med Traffic. 28 3. Structure of Container Trade in the Mediterranean. 31 3.1. In Global Perspective 31 3.1. Mediterranean Container Shipping Structure 32 4. Changing Landscape.. 32 Chapter V DEA Methodology and Analysis Results. 34 1. The Concept of Data Envelopment Analysis (DEA)... 34 1.1. DEA Super Efficiency Ranking... 39 1.2. Output Oriented DEA 40 2 .Measuring Efficiency of Mediterranean Container Ports and Terminals Using DEA... 41 2.1. Data and Statistical Analysis 41 2.1.1. Sample Selection.. 41 2.1.2. Inputs and Outputs. 42 2.1.3. Correlation Coefficient among Inputs and Output Factors .. 46 2.2. Analysis Results. 47 2.2.1. CCR Model 47 2.2.2. BCC Model. 53 2.2.3. Ranking Analysis by Super Efficiency Model . 61 Chapter VI Conclusion.. 63 1. Research Findings. 63 2. Limitations of the Study . 66 3. Future Research on Port and Terminal Efficiency. 66 References 68 | - |
dc.language | eng | - |
dc.publisher | 한국해양대학교 대학원 | - |
dc.title | Efficiency analysis and Ranking of Mediterranean container Ports and terminals | - |
dc.type | Thesis | - |
dc.date.awarded | 2011-08 | - |
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