A multiple container loading problem based algorithm for efficient allocation of goods to vehicles
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 current paper deals with the first stage of a simulation-based decision support system called CILOSIM (CITY -- LOGISTICS -- SIMULATION). The objective of CILOSIM is to simulate different urban goods movement scenarios by implementing control policies, access/time restrictions, partnerships, technology and real time information provision for logistical decision making. We present the first module of CILOSIM called to Vehicle Assignment Model. The function of Goods to Vehicle Assignment Model is to optimally allocate goods to vehicle which depends upon the product configuration, vehicle capacity and vehicle-product compatibility. It is modelled with a concatenation of two problems known in operations research namely Zero/One three-dimensional Knapsack Problem and the three-dimensional Bin Packing Problem. The problem consists in choosing among n rectangular parcels (items) characterized by a height, a width, a depth, a time windows, a product type and a weight to be packed into m goods vehicles (knapsacks or bins) characterized by a height, a width, a depth and weight capacity to minimize the empty space in each goods vehicle. The items are packed according to their product compatibility and time windows without exceeding each goods vehicle capacity. Different scenarios are simulated to identify the possible way of goods allocation to vehicles.
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) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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