A disaggregated integer L-shaped method for the bike sharing rebalancing problem with stochastic demands
Bibliographic record
Abstract
This work deals with the bike sharing rebalancing problem with stochastic demands by proposing new formulations, an exact solution method, new valid inequalities, and new lower bounds. The problem is NP-hard, and it arises in the context of bicycle-sharing systems that need to ensure quality of service. The latter is understood as the availability of bicycles and docks to park them in a network of stations. In this problem, each station presents a random request for the pickup or delivery of bicycles, which is disclosed immediately before the departure of the repositioning vehicles from the depot. The problem is framed as a variant of the stochastic vehicle routing problem, and we propose an implementation of the disaggregated integer L-shaped method to address it. To speed up the resolution, we create customized lower-bounding functionals based on a complexity result we developed for the recourse problem. The results of computational experiments show that our implementation is superior to those in the literature, which leads us to generate and propose a new, challenging benchmark set of instances. • We propose a new method for a bike sharing rebalancing problem. • The demand of bicycle at stations is considered stochastic. • We propose a new dynamic programming formulation to compute the recourse. • New valid inequalities and lower bounds are introduced. • Results show that our new approach is superior when compared with the literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".