Aggregate planning through the imprecise goal programming model: integration of the manager's preferences
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Aggregate planning involves planning the best quantity to be produced during time periods in the medium‐range horizon at the lowest cost. Usually, the production manager seeks a plan that simultaneously optimizes several incommensurable and conflicting objectives, such as total cost, level of inventories, level of customer service, fluctuation in workforce, and utilization level of the physical facility and equipment. The goal programming (GP) model is one of the best known multi‐objective programming models that considers simultaneously several conflicting objectives to select the most satisfactory solution among a set of feasible solutions. In the production planning problem, the goals and the technological parameters are naturally imprecise. Moreover, the existing GP formulations developed in industrial engineering and aggregate production planning do not explicitly incorporate the manager's preferences. The aim of this paper is to develop a GP formulation within an imprecise environment where the concept of satisfaction function will be utilized to explicitly introduce the manager's preferences into the aggregate planning model.
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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 | 0.000 | 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