OFFSHORE PORT ALLOCATION AND INVESTMENT: AN OPTIMIZATION FRAMEWORK
Bibliographic record
Abstract
Appropriate port location selection is critical to achieve the competitiveness and effectiveness of transportation, distribution, and the entire global supply chain and to bolster the local, regional, and national economies. The objective of this study is to develop a new framework to select the optimal location for the offshore port. The framework includes multicriteria decision-making (MCDM) methods along with experts’ judgment and a simulation-based model of the facility's performance. At first, the Rough Analytical Hierarchy Process (AHP) and Rough Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are utilized to preliminarily prioritize the alternative locations. After that, the port performance of selected locations with their corresponding transport distances over the lifetime of the project is assessed via simulation-based experiments. Finally, life cycle costing (LCC) is performed for the final assessment of each port location. The proposed framework is examined for Cua Lo Petrol Base in Vietnam as a case study. The result of the study indicated that the optimal location unveiled by simulation experiments is not always the first-ranking location based on the MCDM. The outcome of this study will assist port and marine investors to find the optimum location for port planning in terms of technical and economic viewpoints.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".