Introduction: 2010 Daniel H. Wagner Prize for Excellence in Operations Research Practice
Bibliographic record
Abstract
Competition for the 2010 Daniel H. Wagner Prize for Excellence in Operations Research Practice provided the five finalist papers featured in this special issue of Interfaces. The prestigious Wagner Prize emphasizes quality and originality of mathematical models along with clarity of written and oral exposition. Authors from IBM's Watson Research Center won the competition with their innovative, diverse suite of models to improve critical choices for managing skilled personnel, choices so important in service industries; their models have been implemented in the IBM Integrated Technology Services business. A finalist team from Georgia Tech joined with the US Centers for Disease Control to solve a physician's often challenging problem of creating the best new schedule for a person who misses one or more previously scheduled vaccinations; the team's optimization approach is implemented in software now freely available to American physicians, and in software with Canadian rules soon to be available to Canadian physicians. A team from Kimberly-Clark Latin America and Penn State University developed optimization models to improve production and inventory control; after overcoming slow solution speeds with heuristics that furnish good solutions in minutes, the models have so far been implemented in Kimberly-Clark plants in five countries. A finalist team from the University of Texas at Dallas worked with Blockbuster, Inc. to improve efficiency and speed in Blockbuster's central system to fill orders from the company's numerous retail stores; implementation of the resulting optimization models led to reduced costs and more balanced workloads in the Blockbuster order-fulfillment center where different processing departments compete for shared resources. Finally, an India-based team from GE Global Research developed models to improve the selection of customers who receive special product offers from a GE commercial bank in Europe; the team's diverse models are now helping the GE bank to improve its customer responses.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.142 | 0.095 |
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".