A Double Optimum New Solution Method Based on EVA and Knapsack
Bibliographic record
Abstract
Optimizing resource allocation often requires a trade-off between multiple objectives. Since projects must be fully implemented or not at all, this issue is modeled as an integer programming problem, precisely a knapsack-type problem, where decision variables are binary (1 or 0). Projects may be complementary/supplementary and competitive/conflicting, meaning some are prerequisites for others, while some prevent others from being implemented. In this paper, a two-objective optimization model in the energy sector is developed, and the Non-dominated Sorting Genetic Algorithm III (NSGA III) is adopted to solve it because the NSGA-III method is capable of handling problems with non-linear characteristics as well as having multiple objectives. The objective is to maximize the overall portfolio’s EVA (Economic Value Added). EVA is different from traditional performance measures and is more appropriate because it incorporates the objectives of all stakeholders in a business. Furthermore, because each project generates different kilowatts, maximizing the total production of the portfolio is appropriate. Data from the Greek energy market show optimal solutions on the Pareto efficiency front ranging from (14.7%, 38,000) to (11.91%, 40,750). This paper offers a transparent resource allocation process for similar issues in other sectors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".