Bibliographic record
Abstract
Computer Number Control (CNC) milling and lathe machines are widely used in manufacturing due to their flexibility in producing parts with a wide variety of geometries. Each flexible machine has a tool magazine capable of holding a set of tools. As machining requirements for each job change, tools can be removed and different ones can be inserted so that the next job can be processed. The existing literature on the job scheduling and the tool loading can be divided into four main areas. The first area is the tool loading for a pre-specified job sequence where the objective is to determine the optimal tool loading by minimizing the number of tool switching. In addition to tool loading, the second area also focuses on sequencing the jobs too; however, the objective is the same as the first one. Rather than to minimizing the number to tool switching, the focal point of the third area has been shifted to minimizing the makespan in presence of multiple process plans. However, the main assumption is that the magazine can hold all tools needed to process all jobs and tool switching is not required. The fourth area considers the geometric and mechanical properties of the tool, assuming a tool switching may be required due to tool life. The job scheduling and the tool loading literatures do not consider multiple process plans or tool life into their problem. Therefore, the first part of this thesis provides a Dynamic Programming method to determine the optimal makespan for a pre-specified sequence of jobs, assuming tool switching may required due to multiple process plans, the capacity of the tool magazine and due to the tool life. In the second part, the assumption for fixed job sequence is relaxed and a heuristic approach is used to first sequence the jobs and then Dynamic Programming is applied to find the optimal makespan for that particular job sequence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".