A Rule-Based Recourse for the Vehicle Routing Problem with Stochastic Demands
Bibliographic record
Abstract
In this paper we consider the vehicle routing problem with stochastic demands (VRPSD). We consider that customer demands are only revealed when a vehicle arrives at customer locations. Failures occur whenever the residual capacity of the vehicle is insufficient to serve the observed demand of a customer. Such failures entail that recourse actions be taken to recover route feasibility. These recourse actions usually take the form of return trips to the depot, which can be either done in a reactive or proactive fashion. Over the years, there have been various policies defined to perform these recourse actions in either a static or a dynamic setting. In the present paper, we propose policies that better reflect the fixed operational rules that can be observed in practice and that also enable implementing preventive recourse actions. We define the considered operational rules and show how, for a planned route, these operational rules can be implemented using a fixed threshold-based policy to govern the recourse actions. An exact solution algorithm is developed to solve the VRPSD under the considered policies. Finally, we conduct an extensive computational study, which shows that significantly better solutions can be obtained when using the proposed policies compared with solving the problem under the classic recourse definition.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".