Partial input to output impacts in DEA: Production considerations and resource sharing among business subunits
Bibliographic record
Abstract
Abstract Data envelopment analysis (DEA) is a methodology for evaluating the relative efficiencies of peer decision‐making units (DMUs), in a multiple input/output setting. Although it is generally assumed that all outputs are impacted by all inputs, there are many situations where this may not be the case. This article extends the conventional DEA methodology to allow for the measurement of technical efficiency in situations where only partial input‐to‐output impacts exist. The new methodology involves viewing the DMU as a business unit, consisting of a set of mutually exclusive subunits, each of which can be treated in the conventional DEA sense. A further consideration involves the imposition of constraints in the form of assurance regions (AR) on pairs of multipliers. These AR constraints often arise at the level of the subunit, and as a result, there can be multiple and often inconsistent AR constraints on any given variable pair. We present a methodology for resolving such inconsistencies. To demonstrate the overall methodology, we apply it to the problem of evaluating the efficiencies of a set of steel fabrication plants. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".