A state of art review on optimization techniques in just in time
Bibliographic record
Abstract
With the development of faster means of communication, better quality computers and rapid transportation systems, manufacturing is no longer restricted at local level, but has become global in character.As a manufacturing company has to become competitive for its survival, it has to supply products of consistent high quality at reliable and reduced delivery time.Market also demands more product variants that means reduced lot size and high flexibility in operations.Manpower cost has also risen.All these factors tend to increase the product cost.However, the industry has to maintain the cost at a reasonable level.Confronting these challenges, companies worldwide are forced to find ways to reduce costs, improve quality, and meet the everchanging needs of their customers.One successful solution has been the adoption of Justintime (JIT) manufacturing strategy in which many functional areas of a company such as manufacturing, engineering, marketing, purchasing etc. are involved.In this paper, literature review on research works based on JIT was carried out and presented.The introductory section deals with the philosophy of JIT, and the concept involved in Kanban optimization and later this paper reviews literature on optimization Technique in JIT implementation.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".