Simultaneous scheduling of machines and automated guided vehicles utilizing heuristic search algorithm
Bibliographic record
Abstract
Proper scheduling of flexible manufacturing systems is considered a key success for industry. Automated guided vehicles are parts of flexible manufacturing systems and are easy to utilize in production systems. Time is directly related to the production costs of all kind. It is necessary to minimize production costs. Improper scheduling of machines and automated guided vehicles may increase the production time. Simultaneous scheduling of machines and material handling systems has many benefits though they are not challenges free. Scheduling of machines and automated guided vehicles, if considered separately, are NP-Hard problems regardless of being considered together or separately. There are two types of scheduling problems according to the literature. Review of literature show that although offline scheduling of machines and vehicles has been studied in a great detail, lack of studies related to dynamic scheduling of machines and automated guided vehicles is very visible. Therefore, the main focus of this study is simultaneous scheduling of machines and automated guided vehicles. First, a heuristic scheduler is designed, in MATLAB software, to propose solutions for simultaneous scheduling of machines and automated guided vehicles in flexible manufacturing systems. Then, a time frame is applied to the offline test problems from the literature to produce dynamic scheduling problem. The methodology in this study is applied on a sample test problem from previous studies for validation purpose. Furthermore, the new dynamic scheduling was performed by producing time tables for pre-defined time frames. The sample test problems are then mathematically modeled to represent the limitations and constraints of the offline and dynamic scheduling problem.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".