Availability consideration in the optimal selection of multiple-aspect RMS configurations
Bibliographic record
Abstract
Machine availability has a profound influence on the performance of manufacturing systems. This paper extends a model for optimizing reconfigurable manufacturing systems (RMS) configurations with multiple-aspects to incorporate the effect of machine availability using the universal generating function (UGF). Two powerful meta-heuristic optimization techniques, namely genetic algorithms (GAs) and tabu search (TS), are used for optimizing the capital cost and system availability of the RMS configurations. The optimized configurations can handle multiple-parts and their structure is that of flow lines allowing paralleling of identical machines in each production stage. The various aspects considered in the RMS configurations include arrangement of machines, equipment selection and assignment of operations. A case study is presented and implementation of the optimization model is carried out using MATLAB software. The results of using both GAs and TS to solve the problem are then reported and compared for validation. Analysis of different cases of availability consideration including infinite and no buffer capacity is performed and results are compared to those obtained when machine availability is not considered. It has been shown that considering availability affects the optimal configuration selection and increases the required equipment. This increases the costs of the near-optimal configurations obtained especially in the case without buffers. The presented model can support the manufacturing systems configuration selection decisions at both the initial design and reconfiguration stages.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".