Optimizing Production Scheduling for Decarbonization in Off-Site Construction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The construction industry is under increasing pressure to mitigate its environmental impact.In Canada, the building sector ranks as the third-largest contributor to carbon emissions, accounting for 13% of the nation's total emissions.This underscores the urgent need for innovative construction paradigms to address environmental challenges (e.g., carbon emissions).While off-site construction (OSC) presents a promising solution due to its potential to reduce carbon emissions, OSC production factories face critical challenges in identifying an effective production sequence to minimize CO emissions.Thus, developing an effective production scheduling method to minimize CO emissions during the production stage is crucial.To address these challenges, this paper proposes an optimal production scheduling framework aimed at minimizing CO emissions during the production stage in OSC.The methodology consists of two key procedures: (i) data collection and analysis to quantify CO emissions for each panel at each workstation; and (ii) the development of a genetic algorithm (GA)-based optimization model to reduce CO emissions through production sequencing.The proposed method is applied to a wood-based panelized wall production factory in Edmonton, Canada.The results demonstrate that the proposed optimization model effectively reduces CO emissions by 4,000 kg annually from a single production line (i.e., the wall production line), thereby enhancing the environmental performance of OSC.This research offers a novel framework for quantifying and mitigating CO emissions in OSC production through sequencing optimization, making a significant contribution to sustainable construction practices.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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