Optimizing Ticket Assignment Through Group Role Assignment with Agents' Busyness Degree
Bibliographic record
Abstract
The optimal assignment of Information Technology (IT) tickets is a critical solution in our ever-expanding digital age. To maximize efficiency and minimize the burden on IT staff, this article proposes a novel approach: the Stacked Role Assignment (GRAABD-WS). GRAABD-WS leverages the principles of E-CARGO, an object-oriented framework designed to solve complex systems. The core concept involves mapping the relationship between tickets and technicians, described as roles and agents, respectively. We evaluate the suitability of technicians for specific tickets by assessing their individual workloads and skill sets, resulting in an “Agent Fitness” value. This value varies based on the roles required by each ticket. By ranking technicians based on their abilities and current workloads, GRAABD-WS ensures that the most suitable technicians are assigned to each task. The effectiveness of this algorithm is demonstrated by its ability to optimally assign 11 agents to 40 tickets within 3.71 seconds. The results highlight the potential of the Stacked Role Assignment algorithm to enhance IT ticket management, providing a robust solution for dynamic and complex IT environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".