Sustainable multi-objective scheduling for automatic guided vehicle and flexible manufacturing system by a grey wolf optimization algorithm
Bibliographic record
Abstract
The simultaneous scheduling decisions between production systems and material handling systems are highly significant for a substantial reduction in makespan and improvement in throughput of flexible manufacturing system resources. In the absence of appropriate scheduling of production resources, the optimum utilization of FMS resources is not harnessed which turns into wastage of resources. In the present study, investigations are carried out for the sustainable multi-objective scheduling of automatic guided vehicle and flexible manufacturing system by the application of a grey wolf optimization algorithm (GWO). Initially the Giffler and Thompson (GT) algorithm [Giffler, B., & Thompson, G. L. (1960). Algorithms for solving production scheduling problems. Operations research, 8(4), 487-503.] along with four different priority hybrid dispatching rules (PHDRs) are applied for the development of the production center schedule thereafter the grey wolf optimization algorithm is applied for the yield of the sustainable multi-objective schedul-ing of automatic guided vehicles (AGVs) and the FMS together with an objective to minimize the total distance travel and number of backtracking of cruising automatic guided vehicle in the U type flexible manufacturing system facility. The applied methodology is evaluated by conducting computational experiments on a benchmark flexible manufacturing system configuration considered from the literature. The results obtained from the computational experiments clearly show that the proposed application of grey wolf optimization algorithm outperforms the other applied procedures in the literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".