Enhancing Strategic Management Through Linear Programming: A Comparative Study Involving Doolittle’s and Simplex Methods
Why this work is in the frame
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Bibliographic record
Abstract
This research paper explores the application of Linear Programming (LP) as a strategic decision-making tool across diverse domains such as agriculture, management, site selection, services, investment, and transportation, with the overarching aim of maximizing profitability.The study introduces Octagonal Fuzzy Numbers (OFNs) and proposes a novel approach for defuzzification using a ranking function derived from Pascal's triangle to handle the left and right spreads of OFNs effectively.To obtain optimal solutions, the formulated LP problems are solved using Doolittle's method, the Simplex method, and the Graphical method.A comparative analysis of the results obtained from these techniques is carried out to determine the most optimal solution.The findings demonstrate the practical applicability and efficiency of LP in real-world scenarios and underscore the advantages of incorporating octagonal fuzzy numbers in uncertain decision-making 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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