A Dynamic Just‐in‐Time Component Delivery Framework for Off‐Site Construction
Bibliographic record
Abstract
Off‐site construction entails various advantages compared with the traditional construction method; however, the fragmentation of the prefabrication and assembly results in a complex supply chain. Both general contractors and factories often encounter production deviation, making the original component delivery plan nonoptimal. Traditionally, both parties tend to rely on internal resources or third‐party resources to manage schedule changes, paying little attention to the optimisation of the component delivery process. The static compensation mechanisms reported in existing literature require factories to manage demand fluctuations but fail to encourage general contractors to control schedule deviations. Therefore, a dynamic compensation mechanism is proposed to achieve just‐in‐time component delivery, with which a factory shares possible changes for each component’s delivery date to its clients on an inverse Kanban system. First, unfavourable changes for the factory schedule are allocated with surcharges, and the general contractor should compensate the factory if it accepts the date changes; secondly, schedule changes that are beneficial for the factory are assigned as incentives, and the general contractor receives the factory’s incentive upon agreeing to the changes. Based on these two scenarios, genetic algorithm‐based optimisation models are developed to achieve optimal delivery planning solutions. General contractors can obtain an optimal component delivery date to reduce the additional cost when they have changed the assembly schedule. General contractors can also optimise their component delivery schedule to trade their duration flexibility for incentives offered by factories. The models can help both parties to reduce component delivery waste when either side has the motivation to change the original component delivery schedules.
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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 | 0.001 |
| 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 | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".