Automated Master Scheduling for Supply Chain Management in Panelized Construction
Bibliographic record
Abstract
In offsite construction, the panelized method is more complex than the volumetric modular approach, as managing the production, delivery, and installation of individual panels is more demanding and time-intensive than delivering fully assembled modular units.Accordingly, effective coordination among supply chain entities is essential as a means of mitigating the risk of cost overruns and delays.To achieve effective coordination, panelized construction companies develop supply chain master schedules that integrate factory production, transportation, and onsite assembly.However, these scheduling practices remain largely manual and time-consuming, lacking a fully integrated approach to align supply chain operations.This lack of synchronization leads to unstable supply chain performance, underutilized resources, increased costs, and project delays.To address these challenges, this research proposes an automated master scheduling system comprising three core components: (i) heuristic scheduling algorithms to automate master schedule generation, reduce bottlenecks in the supply chain flow, and streamline resource allocation based on demand, availability, and operational hours; (ii) a self-adaptive, genetic algorithm-based multi-objective optimization algorithm designed to optimize key supply chain variables, such as number of resources and project priorities; and (iii) a fully integrated simulation model that represents supply chain entities as agents, defining their relationships and interactions.A prototype of the system is developed and implemented in a case study of a panelized home prefabrication facility.This research advances the automation and optimization of supply chain master schedules in panelized construction by addressing key coordination requirements for multi-line production facilities and incorporating practical considerations for transportation and onsite operations across multiple projects.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".