A Data-Driven Forecasting and Solution Approach for the Dial-A-Ride Problem with Time Windows
Bibliographic record
Abstract
The Dial-A-Ride Problem (DARP) consists of de-signing pick-up and delivery routes for a set of customers with special needs. Particularly, it arises in door-to-door transportation services provided to elderly and impaired people. DARP's main objective is to accommodate as many customers' constraints as possible with minimum operation costs. DARP involves realistic precedence and transit time constraints on the pairing of vehicles and customers. This paper proposes a neural network forecasting approach for DARP with time windows (DARPTW). It develops and compares the results of two-layer and a three-layer artificial neural networks (ANN) which forecast demands, service and travel times based on real-life data provided by a transportation company. Experimental results show that three-layer ANN with hyperbolic tangent (tanh) and sigmoid linear unit (selu) activation functions, coupled with a stochastic gradient descent (SGD) optimizer provide the best forecasting results. This paper also develops a data-driven hybrid adaptive large neighborhood search (DD-HALNS). DD-HALNS selects the local search operators according to their updated success' rates, which are, in turn, guided by a learning mechanism from previous successful moves and cost savings. It applies four hybridization features: simulated annealing, tabu lists, genetic crossovers, and restarts. Experimental results on DARPTW benchmark instances highlight DD-HALNS’ ability to improve best known routing solutions, while its application on real life instances, from the Canadian city/region of Vancouver, confirms its implementability.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".