Optimization under uncertainty for food security (doctoral thesis)
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
Thirty percent of the global population experiences food insecurity due to a lack of sufficient, affordable, and nutritious food, preventing them from living healthy and active lives. Through mathematical optimization and collaboration with food assistance programs, this thesis provides possible solutions to address the complexities and uncertainties of real-world challenges in food security. Methods of optimization under uncertainty, including robust optimization, stochastic optimization, inverse optimization, and tree-based machine learning, are explored and applied to problems arising in three specific food assistance programs. The first two programs are food bank organizations: the Association of Dutch Food Banks (the Netherlands) and Moisson Montréal (Canada), for which optimization methods for investment and routing challenges are studied. For the third program, the United Nations World Food Programme, applications of machine learning provided estimates of the number of children under five suffering from acute malnutrition. In addition to solving real problems faced by these food assistance programs, this thesis advances theory in optimization under uncertainty. A matheuristic is presented that finds feasible solutions for the vehicle routing problem when demand, service, and waiting times are stochastic. Furthermore, a convex reformulation for a class of nonconvex optimization problems is introduced, providing results that are useful in many fields, including inverse optimization and robust optimization.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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