Bibliographic record
Abstract
In recent years, in manufacturing industry, their has been a great deal of interest in capacity planning because the focus is shifting on techniques that determine flexibility of the amount and timing of work center capacity to satisfy the master production schedule. There are several techniques available for preparing work center capacity plans under crisp environment, but there is a scarcity of technique available for finding the required capacity in terms of labor hours under fuzzy environment. In the present thesis, we analyze the Bill of Labor (BOL), Resource Profile (RP) and Capacity Requirements Planning (CRP) approaches under fuzzy environment with a variety of assumptions. Chapter 1 provides an introduction to the concepts of capacity planning problems considered in the thesis, followed by the literature survey in Chapter 2. The capacity analysis under fuzzy envi onment using BOL approach for rough cut capacity planning (RCCP) is considered in Chapter 3. Assuming that all the components of an item are manufactured in the same time period as the end item, i.e. lead-time offsets are considered to zero. Chapter 4 deals with the capacity analysis under fuzzy environment using RP approach for RCCP by including the lead-time dimension in it. Chapter 5 deals with the capacity requirements planning under fuzzy environment. Finally, conclusion, contribution and recommendations for further research on the aforementioned problems are presented in Chapter 6.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".