Genetic Algorithm and Loading Strategy for the DynamicVehicle Routing Problem with Simultaneous Pickup and Delivery
Bibliographic record
Abstract
In the field of operations research, optimizing vehicle routing and scheduling plays a critical role in enhancing economic efficiency while reducing environmental impacts. In particular, the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) is a popular variant of the classical vehicle routing problem (VRP) that places emphasis on operational sustainability and efficiency. Despite its popularity, compared to its static counterpart, hardly any attention has been given to the dynamic variant even though many routing scenarios require re-routing midday as unexpected customer orders arrive. To close this gap, this paper addresses the Dynamic Vehicle Routing Problem with Simultaneous Pickup and Delivery (DVRPSPD), a recently proposed variant of the VRPSPD. A loading strategy is proposed which takes into account the unusual characteristics that arise from combining dynamic requests with simultaneous pickup and delivery requests. This loading strategy is applied in conjunction with a genetic algorithm (GA) which employs an alteration of the popular Best-Cost-Route-Crossover (BCRC). The proposed GA, referred to as GA-BCRCD, alongside the loading strategy, demonstrates significant enhancements in solution quality compared to the memetic algorithm previously applied to these instances. For some instances, the proposed approach finds solutions with more than a 25% reduction in total distance travelled by vehicles.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".