Analytic Hierarchy Process–Simulation Framework for Lighting Maintenance Decision-Making Based on the Clustered Network
Why this work is in the frame
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Bibliographic record
Abstract
The lighting system is an important infrastructure that needs to be maintained to curb degradations caused by aging and other extraneous factors. Facility managers are responsible for system operations and confront challenges resulting from the considerable number of maintenance requests under various limitations (e.g., budget, labor resources). Therefore, maintenance activities need to be evaluated continually to improve their efficiency. As for the lighting system, the choice of maintenance methods [i.e., spot relamping (SR) and group relamping (GR)] has typically been made based on rules of thumb and experience. In this respect, this contribution aims to develop a framework that allows facility managers to use systematic analysis to select the most appropriate relamping strategy. The proposed framework integrates analytic hierarchy process (AHP) and simulation methods based on a preset clustered network. The framework is composed of three phases: relamping cost evaluation, carbon dioxide (CO2) emission evaluation, and comprehensive evaluation for decision making on maintenance alternatives. A case study of lighting maintenance is provided to demonstrate the applicability of the framework to the selection of an optimal relamping alternative in consideration of cost and environmental protection. Finally, a sensitivity analysis is conducted to better understand the effect of variations in the clustered network and the importance of environmental protection in the choice of the lighting maintenance procedure.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it