A multi-constrained maintenance scheduling optimization model for a hydrocarbon processing facility
Bibliographic record
Abstract
A maintenance scheduling optimization model considering equipment risk, total maintenance cost, system reliability and availability is proposed. This work is motivated by gas processing operator’s concern of high maintenance cost, poor availability and reliability caused by inefficient maintenance scheduling. The approach presented in this article addresses the optimization of maintenance cost by efficiently scheduling maintenance task subject to reliability and availability constraints. Four maintenance actions are considered for each equipment, namely, corrective, replacement, maintenance and inspection. The proposed solution develops maintenance schedules for complex repairable system with equipment operating in series. Two single-objective, nonlinear mathematical models are presented to find the optimal maintenance cost subject to reliability and system reliability subject to availability constraint. A goal programming model is also proposed to simultaneously deal with multiple criteria based on their importance and defined goals. A gas absorption system of a hydrocarbon processing facility is used to ensure the practicality of the proposed formulation to real industry problems. A comparison of existing and proposed formulation is carried out to show that the proposed optimization approach is an efficient method for optimizing maintenance schedules and flexibility to adjust schedules in a complex operating system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".