Energy-efficient optimization of Flexible Job Shop Scheduling and Preventive Maintenance
Bibliographic record
Abstract
In recent years, there has been growing concern on energy efficiency in the manufacturing enterprises. Since scheduling problem has a direct impact on energy consumption, developing the effective production scheduling is among the priorities in industries. Moreover, in practice, production and maintenance operations have been viewed as major source of energy consumption in industrial system. In this paper, we propose a stochastic mathematical model for a joint production and maintenance operations scheduling problem in a flexible job shop industrial environment in which both traditional and energy efficient aspects are modeled. The objective of this research is to minimize the expected makespan in the scheduling problem focusing on C02emissions reduction in an actual workshop which breakdowns can happen at any moment and make machines unavailable for processing operations. In fact, energy usage associated with the C02emissions of the industrial shop floor are formulated in the constraints with respect to different states of operation and idle. To address this problem effectively, the Genetic Algorithm (GA) is applied for the proposed stochastic model to minimize the expected makespan. From an operation management viewpoint, the proposed model provides a scientific and helpful guideline for manufacturing system to plan production and maintenance simultaneously, with both economic and environmental benefits.
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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 | 0.001 |
| 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".