Constraint-based genetic algorithm for earthmoving fleet selection
Bibliographic record
Abstract
This paper presents a constraint-based genetic algorithm dedicated to optimizing earthmoving operations. The algorithm aims to minimize the total cost of earthmoving operations, accounting for efficient use of the selected equipment fleet. Total cost, duration, and utilization of the equipment fleets involved are estimated via computer simulation and passed to the developed algorithm to optimize fleet selection. Users specify the lower limits of equipment utilization as constraints that guide the algorithm search. The developed algorithm has powerful features: (i) it runs in canonical and genitor forms and (ii) it allows normalization of fitness values of chromosomes using three methods, namely inversion, linear ranking, and nonlinear ranking normalization. The algorithm supports roulette wheel and tournament methods for random selection of chromosomes. Crossover can be applied in a discrete or arithmetic form. In addition, the algorithm performs its computations in an efficient manner by elite best-fit chromosome in all generations and by storing chromosome information in its database. The proposed algorithm is explained in the context of a numerical example to clarify its useful features.Key words: earthmoving, equipment selection, optimization, genetic algorithms, computer simulation.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".