Optimal value determination using traditional and newly developed method based on using initial basic feasible solution of a transportation problem using northwest and Russell method
Bibliographic record
Abstract
This paper utilises a transportation problem scenario to conduct a study on optimisation of transportation problems that are formatted as linear programming problem. Initially, Northwest corner rule and the Russell's method are used to obtain the highest initial basic feasible (IBF) solutions and then a Putcha-Bhuiyan method is proposed to obtain an optimal solution. The Putcha-Bhuiyan method provides the optimal solution with fast convergence of transportation problems. This method results in an optimal solution by making appropriate changes to the IBF solution and eliminating the need to conduct iterations using chain reaction or transportation simplex algorithm. To explain and justify the advantages of the Putcha-Bhuiyan method, the solution to the problem scenario was compared with the transportation simplex method. While the justification of the Putcha-Bhuiyan method is with only one problem scenario, it will be very useful for solving multiple and large-scale optimisation problems that are faced in many disciplines. These concepts are dominantly utilised in disciplines like industrial engineering, mechanical engineering, smart manufacturing, and supply chain management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".