A multiple container loading problem based algorithm for efficient allocation of goods to vehicles
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".