An Approach for Managing Operating Assets for Humanitarian Development Programs
Why this work is in the frame
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Bibliographic record
Abstract
Every year, humanitarian organizations assign a sizable portion of their limited financial resources to procure, operate and maintain operating assets, without which service delivery would be nearly impossible. In this study, using vehicles to represent operating assets, we identify policies for sizing and allocating operational capacity to minimize the expected deprivation costs in a humanitarian development context. First, we develop a stochastic dynamic programming model, and then an efficient heuristic policy that considers the interaction of asset purchasing and operating decisions when the budget is uncertain. Based on a dataset provided by a large international organization, we estimate the parameters of our model to run numerical experiments. Results demonstrate the following: (i) Although budget uncertainty increases the expected deprivation costs and decreases capacity utilization, the negative impact of budget uncertainty is mitigated if budget savings between periods is allowed; (ii) a policy for minimizing the expected deprivation costs over time may avoid using all available assets in all periods; (iii) in situations in which the variation in the criticality of missions is large, both the expected deprivation costs and fleet utilization decrease; and (iv) in most conditions, a centralized asset procurement model outperforms a decentralized model, not only in terms of logistic costs but also in minimizing the expected deprivation costs.
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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 | 0.000 |
| 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) | 0.000 | 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 it