Adaptive Weighted Optimization Framework for Multiobjective Long-Term Planning of Concentrate Ingredients in Copper Industry
Bibliographic record
Abstract
The multiobjective long-term concentrate ingredient planning (MLCIP) plays an important role in modern smelting production measurement systems. MLCIP is a constrained multiobjective optimization problem that comprises a series of continuous ingredient stages and numerous decision variables under dynamic environments. The existing on-site ingredient planning methods primarily focus on satisfying short-term production constraints, while neglecting the resulting global impact on long-term objectives. Consequently, the overall duration of ingredient lists and the interest arising from concentrate hoarding cannot be estimated manually. Thus, we formulated MLCIP into a multistage-constrained large-scale multiobjective optimization problem, where the multiple objectives and constraints are formulated by the safety production rules from the smelting system. Furthermore, an adaptive weighted optimization framework (AWOF) is constructed to reduce the dimensions of searching space in the grouping strategy while adaptively determining the termination of weight optimization process. Specifically, a two-stage stochastic coding simulation method is proposed for the handling of dynamic constraints of the MLCIP. Extensive experiments on various benchmark problems and the MLCIP of actual inventory concentration data acquired from a copper industry were comprehensively used to validate the effectiveness of the designed AWOF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".