Task scheduling and management using genetic algorithms with application in production process optimisation
Bibliographic record
Abstract
This paper presents a methodology which uses Genetic Algorithms (GA) for task scheduling and management in an environment where multiple jobs compete for a limited number of resources. The primary objective of the developed system of task scheduling and management is to minimise the cost of resources using available resources while ensuring that the jobs are completed within stipulated timeframes while meeting the task specifications and performance criteria. Once the resources are allocated by the GA-based scheduling algorithm to complete a given set of jobs, it is necessary to continuously monitor the progress of jobs and changes in the environment and in the event of such situations as performance degradation and machine breakdowns, to plan, allocate and rearrange the resources to achieve the system objective. In this paper, a technique is developed to accommodate machine breakdowns and unavailability of machines due to prior assignment or maintenance. Another feature of the developed algorithm is the use of domain knowledge about the process to expedite the evolution process. In the present paper, methodology is also developed to accommodate high priority jobs that may be introduced after the initial scheduling. The developed methodology is applied to plan the activation and post activation processes in an activated carbon manufacturing plant and to schedule and manage the resources such as kilns, crushers, blenders, washers and dryers in the plant. The results demonstrate the effectiveness of the developed approach.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".