Enhanced Cross-Dock Productivity: Combining Self-Driving Vehicles with Forklifts
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
A cross-dock (CD) in a supply chain avoids storing goods that would be picked for orders soon after. Vehicles inbound to the CD are unloaded and their contents are re-sorted. Appropriate items are then loaded within a short time on outbound vehicles for shipment to customers. The CD material handling operations of unloading, sorting and loading are typically done “manually”, by forklifts with human operators. In this chapter, we consider the replacement of some or many forklifts by “Self-Driving Vehicles” (SDV). Can the resulting semi-automated material handling system attain the same or greater productivity as the fully manual system? At what cost (per unit of output)? We develop simulation models of two CDs, one purely manual and the other containing a mixture of forklifts and SDVs. Several CD performance measures are defined and estimated via simulation. For each CD, response surface methodology is employed to determine a near-optimal set of material handling equipment, when that CD is operated at a specified performance level.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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