A Decision-Support Model Utilizing a Linear Cost Optimization Approach for Heavy Equipment Selection
Bibliographic record
Abstract
In heavy earthwork operations, optimizing equipment selection based on economical operational analyses has a primary role in the success of major construction projects. The main objective of this study is geared towards developing an automated optimization model in order to assist contractors in this multifaceted task. Economical operation analysis is conducted for an equipment fleet while taking into consideration the owning and operating comprehensive costs involved in most of earthwork operations. The proposed model is developed in a Microsoft environment using Visual Basic for Applications® (VBA) and is capable of being integrated with other estimating and optimization or simulation models. The implementation of the model provides optimum equipment fleet to perform earthwork operations based on their economical operation analysis by providing the user with a final optimized report that includes ownership and rental options. The model is validated through an actual case project to illustrate its numerical capabilities and to quantify its degree of accuracy. The results of this study are anticipated to be of major significance to contractors and would contribute to the database of fleet management systems by including a computer model that integrates heavy equipment operational analysis with its corresponding comprehensive economical analysis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".