Multi-item order quantity optimization through stochastic goal programing
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
Abstract Managing multi-item economic order quantity (MIEOQ) problems within an uncertain business environment is a critical challenge. Decision-makers, with a comprehensive understanding of organizational goals and risk tolerances, play a pivotal role in this context. However, existing solutions often inadequately consider decision-maker preferences in MIEOQ problem-solving. The literature suggests that integrating the concept of satisfaction function with stochastic goal programming (SGP) can address this issue. However, the existing SGP approaches struggle with the challenge of effective goal setting. Additionally, employing distinct satisfaction functions for each uncertain goal can complicate threshold setting, diminishing their effectiveness. To tackle these challenges, we introduce a straightforward, yet effective approach called aspiration-free goal programming (AFGP) and integrate it with a unified satisfaction function. AFGP operates by minimizing expected values of deviation variables, eliminating the challenging task of goal setting under uncertainty. A unified satisfaction function is a singular metric applied uniformly across multiple goals, offering a consistent framework for evaluating performance across diverse objectives. This integration forms a preference-sensitive framework that not only captures nuanced trade-offs between conflicting objectives but also enhances decision quality and stakeholder satisfaction. By emphasizing the importance of decision-maker’s preferences and addressing identified issues, our research introduces a practical and effective approach for achieving balanced solutions in uncertain MIEOQ environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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