Unequal area facility layout problem considering transporters interaction– a queuing theory and machine learning approach
Bibliographic record
Abstract
This study presents a novel analytical framework that merges queueing theory with deep neural networks to optimize facility layout and transporter selection in manufacturing systems. It addresses critical factors such as stochastic service times of facilities, random demand, transporter capacity, speed, and transportation batch size. Three objectives are considered: minimizing material handling costs (MHC), work-in-process (WIP), and the interaction probability of transporters (IP). The latter objective focuses on reducing instances where transporters cross paths to prevent accidents or disruptions. WIP is computed using a multi-class open queueing network model, while IP is determined using a deep neural network. The model facilitates the identification of facilities and the assignment of suitable transporters, considering empty transporter travels to minimize MHC and WIP. Results from the model are compared to a simulation model for validation across various scenarios, demonstrating acceptable accuracy. Additionally, a multi-objective meta-heuristic optimization algorithm is employed to solve the model. The effectiveness of the optimization method is evaluated against other approaches, highlighting its applicability in enhancing manufacturing system performance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".