The Northern Sea Route vs the Suez Canal Route: An Economic Analysis Incorporating Probabilistic Simulation Optimization of Vessel Speed
Bibliographic record
Abstract
While previous studies have examined the economics of shipping from Europe to Asia via the Northern Sea Route (NSR) versus the Suez Canal Route (SCR), most have not adequately accounted for the variability in input parameters such as the cost of fuel or the amount of ice encountered on a voyage. Furthermore, no prior study has attempted to utilize speed optimization as part of the analysis. Because the rate fuel consumption for propulsion is intrinsically linked with vessel speed, reducing speed can create the potential for large savings in fuel costs, along with the added benefit of reduced emissions. However, the reduced speed means a longer transit time, meaning increases in other time based costs, such as daily pay for a ship’s crew. The question then becomes what is the optimal speed? This paper examines the use of speed optimization to determine whether it is potentially more profitable for a container shipping company to ship from Rotterdam to Yokohama through the SCR year round (Option A) or to ship through the NSR during the months it is passable while using the SCR for the remainder of the year (Option B). A probabilistic model is presented to determine average per trip profits for both options A and B. This model is used in conjunction with a simulation optimization technique to determine the optimum speeds to maximize average per trip profitability for each option. The results show that probabilistic simulation optimization of vessel speed may better inform shipping companies as to the financial impacts of the speeds that they choose to use for shipping and thus enable better decision making as it pertains to both route choice and ships’ speeds. The analysis indicates that speed optimized container shipping year round through the Suez Canal appears to be the more profitable of the 2 options.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".