Modeling Resilience Enhancement Strategies for International Express Logistics
Bibliographic record
Abstract
International express is a most time-sensitive industry, and members of this industry must be able to respond to disruptions quickly to ensure service quality and to avoid a loss of their competitiveness with other logistics service providers. Instead of a method that arbitrarily makes rushed decisions during the postdisruption phase, this paper describes a method for quantifying and optimizing resilience strategies based on concepts of integrated resource assignment, regardless of where the available resources are located in the logistics network studied or how much capacity can be rented from others. The study started with the use of a typical transportation network modeling approach and then incorporated nonlinear time-dependent cargo value functions into a multiobjective mixed-integer nonlinear programming problem. A set of optimal actions from resilience strategies, such as the selection of alternative routes, switching of shipping modes, rental of other carriers' capacities, reallocation of local trucks, and prioritization of the order of shipments because of limited capacities, was considered. Decisions should be based on overall trade-off considerations and, at the same time, joint maximization of the product of the total time-dependent cargo value and the corresponding throughput and minimization of the costs incurred with resilience enhancement strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".