Tactical capacity planning in an integrated multi-stakeholder freight transportation system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study focuses on investigating tactical planning of an integrated multi-stakeholder system. The system receives time-dependent requests from carriers and shippers and optimizes in time and space the operations and transportation activities through consolidation of loads of different shippers into the same vehicles and synchronization of activities. The aim of tactical planning in this system is to build an efficient service network and schedule to satisfy the regular demand and requirements of shippers by making use of the predicted services and their capacities offered by the carriers. The decisions to be made are to select the transportation services offered by carriers, schedule the services that will operate, select the shipper-demand requests to move, identify the demand itineraries, and find the terminals where the shipments need to be stored temporarily with the objective of maximizing the profitability of the system. A novel mathematical formulation of the problem is introduced. Extensive computational analysis is performed on the split and unsplit shipment-flow versions of the problem to evaluate the solution potential of the proposed model with emphasis on the effect of the choice of parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it