Application of Dynamic Programming in Long-Range Pipeline Facility Planning and in Assessing Off-Design and Off-Flow-Forecast Trajectories
Bibliographic record
Abstract
Dynamic programming (DP) inherently provides a methodology for evaluating a series of decisions in order to determine an optimal policy or path forward. The methodology basically enumerates and evaluates alternative states over the planning horizon in formulating the optimum strategy. In the present work, the concept of DP has been applied to pipeline long-range facility planning problems, and further extended to allow evaluation of nth optimum pipeline facility deployments based on cost and/or probabilities of constraints. The best four options were further analyzed considering uncertainties in the cost elements and the resulting economic risk associated with each optimum path. This paper presents the theory behind the extension of the DP methodology to pipeline long-range facility-planning problems over a planning horizon that considers inherent uncertainties in gas supply and demand as well as a range of available facility options. Uncertainties in the size and location of the required facilities to handle the forecast volumes, and associated variances in their respective cost to build and operate the various facilities, are all accounted for. The problem is further complicated by the possible changes in the expected flow from that forecast during design and the resulting penalties associated with the under- or over-sizing of facilities. It was demonstrated that it is important that the off-design flow forecast be evaluated to determine the impact of future variability or changes. The value that the organization can derive from being able to quantify the benefit (or penalty) of forecast uncertainty and over- or under-building long-range facilities, is significant.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".