A Simulation-Based Decision-Support System for Reducing Duration, Cost, and Environmental Impacts of Earthmoving Operations
Bibliographic record
Abstract
Earthmoving operations are equipment-intensive processes that rely heavily on the proper selection of the equipment fleet and proper scheduling of associated tasks. Early equipment planning decisions have direct implications on schedules, costs, and more importantly, the environmental performance of such operations. While traditional planning of earthmoving works is ad-hoc and based on planners' experiences, ensuring favorable performance requires advanced analytical techniques that consider multiple variables and competing objectives. Accordingly, this study develops a discrete-event simulation-based decision-support system (DES-DSS) for selecting the optimal equipment fleet, while considering the trade-offs between time, cost, and environmental impacts. The model's results from a case study reveal how different fleet mixes and sizes can considerably impact associated emissions, durations, and costs. The DES-DSS can aid planners in making informed decisions during early planning stages and be used as a control feedback mechanism to continuously enhance operations in real-time while reducing emissions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".