A simulated annealing algorithm for dynamic system reconfiguration and production planning in cellular manufacturing
Bibliographic record
Abstract
Most manufacturing system design problems have been studied under static conditions in which the facilities are configured on fixed shop floors for relatively long planning period by assuming constant product mix and demand. In today's dynamic business environment, shorter time periods should be considered where the product mix and demand may vary from period to period. As a result, the best facility layout for one period may not be efficient for subsequent periods. To address this issue, several authors proposed dynamic system reconfiguration models and solution procedures for manufacturing system design. In this paper, we consider an integrated problem of production planning and dynamic system reconfiguration in cellular manufacturing systems where production quantities are also decision variables. Based on this consideration, we propose a mathematical programming model for solving this problem. The solution of the model provides the planned production quantity, inventory level and system configuration for each period. Since the problem is NP-hard, we developed a heuristic algorithm based on multiple Markov chain simulated annealing to allow multiple search directions to be traced simultaneously. Numerical examples are presented to demonstrate the features of the proposed model and the computational efficiency of the developed algorithm.
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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".