Hybrid Genetic Algorithm-Simulation Optimization Method for Proactively Planning Layout of Material Yard Laydown
Bibliographic record
Abstract
This paper presents a hybrid optimization method combining a genetic algorithm (GA) and simulation for planning the layout of material yard laydown areas. An optimized material yard layout entails efficiency in terms of time and cost for decision makers who seek increased performance in material handling, availability, and accessibility. Laying out materials on yards is mostly performed reactively in current practice, where the planner decides daily where to position the incoming materials, based on the list of material arrival and required materials for consumption, received daily. This policy cannot account for the dynamism of material flow in and out of the yard during a construction project. In contrast, a proactive materials placement policy can be used to address this concern based on incoming and outgoing material schedules for a certain period of time. This paper aims to evaluate the proactive material placement policy and present an integrated framework to determine the optimum layout for placing materials resulting in minimum material haulage time. To this end, a hybrid optimization is implemented through a case study from the steel fabrication industry, where an effective materials handling method could be of great significance. The major contribution of this work is the development of an approach that performs dynamic layout optimization of materials arriving at construction yards, using GA to heuristically search for the solution, and use of simulation to model the material handling process and determine the material haulage time. Results of the analyses show clear merits of proactive material placement over the reactive strategy and demonstrate the importance of GA and simulation integration to obtain more realistic outcomes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".