How to operate ship fleets under uncertainty
Bibliographic record
Abstract
Ships operated by a liner company are scattered around the world to transport goods. A liner company needs to adjust its shipping network every few months by repositioning its ships to respond to uncertain container shipping demand. Few studies investigate a liner company's multiperiod heterogeneous fleet deployment problem under uncertainty, considering fleet repositioning, ship chartering, demand fulfillment, cargo allocation, and adaptive fleet sizes. To this end, this study formulates a mixed‐integer linear programming model that captures all of these elements. This study also designs a Benders‐based branch‐and‐cut algorithm for this non‐deterministic polynomial‐time (NP)‐hard problem. Two types of acceleration strategies, including approximate upper bound tightening inequalities and Pareto‐optimal cuts, are applied to improve the performance of the algorithm. Extensive numerical experiments show that the proposed algorithm significantly outperforms CPLEX and its Benders decomposition framework in solving the model. We conduct an intensive analysis and find that multistage stochastic programming can lead to better solutions than two‐stage stochastic programming. We also find that 10% of the benefit provided by the multistage model over the two‐stage model is due to better fleet deployment decisions and that 90% of the benefit is due to better demand fulfillment and allocation decisions. By exploring three practical questions regarding driver analysis of liner company profitability, benefits analysis of adaptive fleet sizes, and the influence of the COVID‐19 pandemic on liner shipping, we show how liner companies can benefit from managerial insights obtained in this study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".