Ant colonies for structure optimisation in a failure prone series‐parallel production system
Why this work is in the frame
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Bibliographic record
Abstract
Purpose The purpose of this paper is to extend the optimal design problem of series manufacturing production lines to series‐parallel lines, where redundant machines and in‐process buffers are both included to achieve a greater production rate. The objective is to maximize production rate subject to a total cost constraint. Design/methodology/approach An analytical method is proposed to evaluate the production rate, and an ant colony approach is developed to solve the problem. To estimate series‐parallel production line performance, each component (i.e. each set of parallel machines) of the original production line is approximated as a single unreliable machine. To determine the steady state behaviour of the resulting non‐homogeneous production line, it is first transformed into an approximately equivalent homogeneous line. Then, the well‐known Dallery‐David‐Xie algorithm (DDX) is used to solve the decomposition equations of the resulting (homogenous) line. The optimal design problem is formulated as a combinatorial optimisation one where the decision variables are buffers and types of machines, as well as the number of redundant machines. The effectiveness of the ant colony system approach is illustrated through numerical examples. Findings Simulation results show that the analytical approximation used to estimate series‐parallel production lines is very accurate. It has been found also that ant colonies can be extended to deal with the series‐parallel extension to determine near‐optimal or optimal solutions in a reasonable amount of time. Practical implications The model and the solution approach developed can be applied for optimal design of several industrial systems such as manufacturing lines and power production systems. Originality/value The paper presents an approach for the optimal design problem of series‐parallel manufacturing production lines.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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