Cross-Assets Trade-off Analysis: Why Are We Still Talking About It?
Bibliographic record
Abstract
While strategic level transportation asset management need to be driven by cross-asset trade-off analysis, the literature has been lacking in the techniques and tools to support global optimization and trade-off across asset types. This paper presents a cross-asset optimization and demonstrates its application to the strategic, very long-term (20+ years) planning for mixed assets. The case study is based on actual and complete dataset of four types of transportation assets of the province of New Brunswick, Canada. The optimization and trade-off analysis for this paper was carried using a tool called TAMWORTH. The cross-asset trade-off approach in TAMWORTH is based on linear programming and innovative improvements that reduces the problem size, and facilitates rapid solution of the multi-period optimization problem. With these innovations, TAMWORTH is capable of applying global optimization to conduct cross-asset trade-off analysis for over 25 years for the full set of transportation assets. The case study results show that an objective function based on condition maximization outperforms a cost minimization objective at target level of conditions. The nature of trade-off of activities, both in type of treatment and asset, and the timing is very complex, and hence impossible to replicate using engineering judgement. Given the associated obvious economic gains of cross-asset trade-off analyses, it is worth the effort of adopting advanced analysis tools that deploys true mathematical optimization to support the strategic long-term planning in transportation asset management.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".