Optimizing Production Scheduling for Decarbonization in Off-Site Construction
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 CO₂ emissions.Thus, developing an effective production scheduling method to minimize CO₂ emissions during the production stage is crucial.To address these challenges, this paper proposes an optimal production scheduling framework aimed at minimizing CO₂ emissions during the production stage in OSC.The methodology consists of two key procedures: (i) data collection and analysis to quantify CO₂ emissions for each panel at each workstation; and (ii) the development of a genetic algorithm (GA)-based optimization model to reduce CO₂ 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 CO₂ 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 CO₂ emissions in OSC production through sequencing optimization, making a significant contribution to sustainable construction practices.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".