Exact First-Choice Product Line Optimization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Which products should a firm offer based on its customers’ preferences? This is the question posed in the problem of product line design, a well-studied and notoriously difficult problem that is central in marketing science. In “Exact First-Choice Product Line Optimization” by Dimitris Bertsimas and Velibor V. Mišić, the authors propose a new approach for solving this problem when segments of customers choose products according to a ranking. They propose a new mixed-integer optimization model of the problem, which they show to be tighter than prior formulations, and a solution approach based on Benders decomposition, which exploits the surprising fact that the subproblem can be solved efficiently for both integer and fractional master solutions. A well-known product line instance based on a conjoint data set of over 3,000 products and 300 respondents, which required a week of computation time to solve in prior work, is solved by the authors’ approach in just over 10 minutes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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 it