A Framework for Modelling Reconfigurable Manufacturing Systems Using Hybridized Discrete-Event and Agent-based Simulation
Why this work is in the frame
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Bibliographic record
Abstract
Important objectives and challenges in todayʼns manufacturing environment include the introduction of new products variants and the designing and developing of reconfigurable manufacturing systems. Due to changing customer requirements, products development time is shorter and manufacturing systemsʼn ability to physically reconfigure is important. The objective of this research is to investigate and support the reconfigurability of a manufacturing system by applying hybridized Agent-based and Discrete-Event simulation modelling technique. Emergent behaviour of the simulation model, when various modifications take place in the system, is examined. The benefits of this framework are decentralized control and collaborative decision making using the UML object-oriented modelling technique, flexible reaction to system changes in terms of product variety and possible system disturbances, and system performance improvement. AnyLogic multi-method simulation modelling platform is utilized to design and create different types of agents. The proposed simulation model results are demonstrated and verified in a case study using the configurable assembly Learning Factory (iFactory) in the Intelligent Manufacturing Systems (IMS) Center at the University of Windsor. The benefits and limitations of the proposed framework are discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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