Solving dynamic facility layout problem using a hybridized heuristic dynamic programming approach
Bibliographic record
Abstract
Today’s market volatility shortens product lifecycles and drives constant changes in product mix and demand. Changes in product mix and demand necessitate, in turn, changes to the shopfloor layout. The dynamic facility layout problem (DFLP) addresses layout development over multiple periods, with rearrangements from one period to another. To optimize the DFLP using dynamic programming (DP), the DP state space should be restricted as the problem is NP-hard. Therefore, a two-phased hybridized solution algorithm is being proposed and developed in this article. In the first phase, a heuristic approach is used to determine the set of layouts to be considered in each period. In the second phase, a metaheuristic approach is used to solve the recursive formulation of DP. A genetic algorithm (GA) searches for the best subsets of layouts, each represented by one chromosome. Notably, the GA incorporates a heuristic selection operator guided by a deep neural network algorithm. The best subset of layouts that results in the best multi-period layout plan is found throughout the different GA generations. The proposed method’s efficiency is statistically validated through rigorous statistical tests, affirming its superior performance, particularly for large-sized instances of the problem, and showcasing more efficient solutions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".