Tuning the Parameters of a Memetic Algorithm to Solve Vehicle Routing Problem with Backhauls Using Design of Experiments
Bibliographic record
Abstract
AbstractVehicle Routing Problem with Backhauls (VRPB) is an extension of the general Vehicle Routing Problem (VRP). In contrast with general VRP, VRPB considers two types of linehaul and backhaul customers. VRPB tries to find optimal routes with minimum cost in which backhaul customers are visited after linehaul customers for a fleet of heterogeneous vehicles. In this paper, a Memetic Algorithm (MA) is developed to solve the VRPB. Similar to other metaheuristic algorithms, an important issue that affects the performance of MA is the selection of components employed in the algorithm along with their parameters ’ values. This work examines the effect of employing different combinations of MA components and parameter values on both the algorithm’s efficiency and the quality of solutions. Design of Experiments (DOE) is introduced as a systematic approach to find the best combination of these parameters ’ values. Analysis of variance (ANOVA) is used to analyze the main effect and interaction effects of the considered parameters. Results verified the efficacy of the proposed MA method and the systematic tuning approach for MA to solve VRPB. KeywordsMemetic algorithm, Design of experiments, Metaheuristics, Vehicle routing problem 1.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".