Enhanced Generalized Reduced Gradient Algorithm for Multi-Product Fish Production Planning with Perishable Inputs
Bibliographic record
Abstract
Perishable-input industries such as fish processing require production plans that minimize cost, waste, and unmet demand amid volatile markets.We cast this task as a mixed-integer nonlinear programming (MINLP) model that embeds freshness windows, labor-capacity ceilings, and stochastic demand across multiple products and periods.To solve the resulting non-convex problem, we design an enhanced Generalised Reduced Gradient (GRG) algorithm-a projection-based gradient method that activates only locally binding constraints and rounds each iterate to the nearest mixed-integer feasible point, speeding convergence.Tested on an Indonesian plant with eight products over four periods, the MINLP-GRG approach cuts total cost by 5.2% versus classical GRG and 2.1% against a commercial mixed-integer linear programming (MILP) solver, reduces spoilage from 8.1% to 3.2% (≈ 60%), and lowers under-delivery by 45.6%, all within 15 s on a standard workstation.A larger case (20 products, eight periods) converges in 78 iterations (< 62 s) with linear memory growth, showing scalability.The proposed framework therefore delivers measurable economic and sustainability gains for fish-processing operations.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".