Selecting a Mix of Dispatching Rules for a Job Shop by Using Artificial Neural Networks
Bibliographic record
Abstract
Dispatching rules are a popular and commonly researched technique for scheduling tasks in job shops. Much of the past research has looked at the performance of various dispatching rules when a single rule is applied in common to all machines. However, better schedules can frequently be obtained if the machines are allowed to use different rules from one another. This research investigates an intelligent system that selects dispatching rules to use on each machine in the shop, based on a statistical description of the routings, processing times and mix of the jobs to be processed. Randomly generated problems are scheduled using permutations of three different dispatching rules on five machines. A neural network is then trained by using a commercial package to associate the statistical description of each problem with its best solution. Once trained, a network is able to recommend for new problems a dispatching rule to use on each machine. Two networks were trained separately for minimizing makespan and the total flowtime in the job shop. Test results showed that the combination of dispatching rules suggested by the trained networks produced better results for both objectives than the alternative of using the one identical rule on all machines.
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 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".