A Continuous Deviation-Flow Location Problem for an Alternative-Fuel Refueling Station on a Tree-Like Transportation Network
Bibliographic record
Abstract
Due to the increasing popularity of alternative-fuel (AF) vehicles in the last two decades, several models and solution techniques have been recently published in the literature to solve AF refueling station location problems. These problems can be classified depending on the set of candidate sites: when a (finite) set of candidate sites is predetermined, the problem is called discrete; when stations can be located anywhere along the network, the problem is called continuous. Most researchers have focused on the discrete version of the problem, but solutions to the discrete version are suboptimal to its continuous counterpart. This study addresses the continuous version of the problem for an AF refueling station on a tree-type transportation network when a portion of drivers are willing to deviate from their preplanned simple paths to receive refueling service. A polynomial time solution approach is proposed to solve the problem. We first present a new algorithm that identifies all possible deviation options for each travel path. Then, an efficient algorithm is used to determine the set of optimal locations for the refueling station that maximizes the total traffic flow covered. A numerical example is solved to illustrate the proposed solution approach.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".