Multi-agent scheduling problems under multitasking
Bibliographic record
Abstract
We consider a multitasking scheduling model with multiple agents, each of which has a set of tasks to perform on a cloudmanufacturing platform on a competitive basis. Each agent wishes to minimise its desirable objective function related tothe completion times of its own tasks only. However, the cloud manufacturing platform wishes to minimise the objectiveof one agent (being long-term critical agent), while keeping the objective of each of the other agents (being short-termone-off agents) within a given limit. The objective functions considered are the maximum of a regular function (associatedwith each task), the total completion time, and the weighted number of late jobs. Cloud manufacturing enables multitaskingscheduling, under which the processing of a selected task may be interrupted by other tasks that are available but unfinished.We ascertain the computational complexity status of each of the problems we consider and devise solution procedures,if viable, for them. We also conduct numerical studies to generate insights into the effects of multitasking on schedulingoutcomes, with which the decision maker can justify making investments to adopt or avoid multitasking.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".