Optimal Replacement of a Fleet of Assets with Economic and Environmental Considerations
Bibliographic record
Abstract
This paper proposes a mathematical model for replacement of a fleet of assets considering technology improvement and varying utilization. The objective is to simultaneously minimize the total ownership cost of a fleet of assets and the total greenhouse gas (GHG) emissions caused by the fleet. The model allows the assets to be kept in storage in any time period, and over which, such assets do not age. We modeled previous utilization to make a more realistic difference between assets. To take into account the economic and environmental factors, we include purchasing new assets, O&M of in-use assets, holding in-storage assets, and salvaging items. Using carbon price of an emission trading market, we convert the GHG emissions of the above items to a monetary value. In addition, GHG cap and budget limit of the fleet owner is formulated. The outputs of the model include optimal decision on which assets should be in-use, in-storage, and salvaged in each period. Additionally, the model determines how many new assets should be purchased and added to the fleet in each period. The applicability of the model is shown by use of data from a fleet of excavators and CPLEX software. The proposed model can help companies to reduce their carbon footprint by employing the new economic-environmental based replacement model, in which GHG emissions of assets are taken into consideration.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".