Improved Bi-Level Mathematical Programming and Heuristics for the Cellular Manufacturing Facility Layout Problem
Bibliographic record
Abstract
A good layout plan results in improvements in machine utilization, setup time, and reduction in work-in-process inventory and material handling cost. Facility layout problem (FLP) for CMS includes both intercellular- and intracellular-layout. Most of the literature takes a discrete approach and rarely considers operations sequence and part demand. In this paper, a novel bi-level heuristic and mixed-integer non-linear programming continuous model for the layout design of cellular manufacturing are developed. Machine tools and manufacturing cells layout are determined sequentially by solving a leader and follower problem, respectively. Facilities are assumed unequal sizes. Both overlap elimination and aisle constraint modeling have been considered. The model is nonlinear; problem is NP-hard. Hence, only small instances of the problem can be solved using the exact linearized model. The developed heuristic is used to solve large instances of the problem. A real case study from the metal cutting inserts industry, where multiple families of inserts have been formed, each with its distinguished master plan, is presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".