Formulation of a pull production system for optimal inventory control of temporary rebar assembly plants
Bibliographic record
Abstract
Temporary fabrication plants such as rebar assembly and precast segment shops are increasingly used in large scale construction projects to provide a construction site of its material needs. A plant needs to be operated in such a way that it is flexible enough to adapt to changing project demands while minimizing inventories. Meeting such needs requires careful control of the level of raw materials and assembly products fabricated in the plant, the two main types of inventories. However, in practice the ordering of raw materials and assembly times are ad hoc, leading to excess inventories and added costs to the project. This paper presents a methodology for effective, efficient, and economic control of inventory levels in temporary rebar assembly plants. Ordering processes are formalized to convert existing approaches into a pull production system. Given this transformation, a methodology is presented that employs Monte Carlo simulation and optimization techniques to identify inventory levels that minimize inventory costs while simulating variability in demand, procurement lead times, and production capacity. A retrospective case on a rebar assembly plant shows that the same amount of work can be performed with significantly less inventory levels when applying the proposed production methodology. It also provides evidence that the cost savings from inventory costs outweigh any additional holding or delivery costs associated with a pull production system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".