Solving the Electric Share‐A‐Ride Problem Using a Hybrid Variable Neighborhood Search Algorithm
Bibliographic record
Abstract
This paper extends the share‐a‐ride problem (SARP) by incorporating electric vehicles (EVs) to reduce greenhouse gas (GHG) emissions, thus addressing environmental concerns. We introduce this new extension as the electric share‐a‐ride problem (E‐SARP). We aim to generate E‐SARP routing plans where EVs serve all passenger and parcel requests while visiting charging stations (CSs) as necessary for recharging. The objective is to maximize total profit from fulfilling passenger and parcel requests. We develop a mixed‐integer programming (MIP) model and propose a hybrid algorithm based on the variable neighborhood search (VNS) framework, integrated with a simulated annealing (SA) acceptance criterion (HVNS). The MIP model provides optimal solutions for small E‐SARP instances using the CPLEX solver, while the HVNS algorithm is designed to solve larger E‐SARP instances. Numerical experiments are conducted to assess the performance of the proposed HVNS and to provide managerial insights, demonstrating that the use of EVs can effectively address environmental concerns without significantly compromising the profitability of the transportation network.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".