Solving Tri-criteria: Total Completion Time, Total Earliness, and Maximum Tardiness Using Exact and Heuristic Methods on Single-Machine Scheduling Problems
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Bibliographic record
Abstract
Machine scheduling problems have become increasingly complex and dynamic.In industrial contexts, managers often evaluate several objectives simultaneously and attempt to identify the optimal solution that satisfies all concerns.This study proposes two heuristic methods based on SPT and dominated rules (DR) to minimize Total Completion ∑ , Total Earliness ∑ , and Maximum Tardiness Time for multicriteria and multi-objective functions (1//(∑ , ∑ , ) and (∑ + ∑ + )) based on single machine scheduling problems.in addition, two exact methods Branch and Bound (BAB with and without DR) and a complete enumeration method are applied to solve the multi-criteria and multi-objective functions.According to the calculation results, the CEM is able to solve problems up to = 11 jobs, while BAB without DR and BAB with DR able to resolve problems from = 19 to = 50 jobs, respectively, within a reasonable time.However, heuristic methods can solve up to = 5000 jobs. in addition, the experimental results for a subproblem show that the heuristic methods can solve up to = 4000 jobs.Practical experiments demonstrate the proposed heuristic methods are the most effective of all approaches.All methods used in this work were coded with MATLAB 2019a.
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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 | 0.001 | 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