Time–Cost–Quality Trade-Off in a Broiler Production Project Using Meta-Heuristic Algorithms: A Case Study
Bibliographic record
Abstract
The global production of broiler meat was forecasted to be 97.8 MT in 2019. The cost fluctuations create risks in production. In order to have an effective management system, process uncertainty must be taken into account. This approach considers the process as an interval with fuzzy numbers and, for managing the risks, it uses the variable α, a parameter determined by the manager in an interval between 0 and 1. Then two algorithms, namely the multi-objective imperialist competitive algorithm (MOICA) and multi-objective particle swarm optimization (MOPSO), were compared and applied. Since the process of production has many activities and each activity has possible options, the process does not have a unique solution. Therefore, the objective function and its assigned weights in terms of time, cost, and quality can be applied to select the best solution from those obtained. A vast amount of uncertainty can be observed, and effective management necessitates dealing with these uncertainty issues. The MOPSO algorithm showed better performance than the MOICA algorithm in this problem. Based on fuzzy logic and influenced by the uncertainty condition (α = 0), time, cost, and quality in the MOPSO and the MOICA algorithms were 1793.8 h, $260,571.7, and 46.66%, and 1792.5 h, $260,585.7, and 51.19%, respectively.
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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 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".