Optimal road facility spare parts location with continuum approximation
Bibliographic record
Abstract
This study presents a new methodology for optimizing the location of spare parts depots for expressway facilities using a continuum approximation (CA) approach. The increasing importance of expressway facility asset management necessitates efficient strategies for minimizing both user impact during failures and the costs of maintaining spare parts depots. In this study, we first formulate a model within the framework of dynamic facility location planning (DFLP), a type of integer programming (IP), to optimize the spare parts location plan, taking into account the failure processes of expressway facilities. Traditional IP models are computationally intensive when applied to large-scale networks. To address this, we adapt the CA approach, traditionally used for facility location problems in Euclidean spaces, to handle network distances by embedding the road network into a new Euclidean space using the Isomap algorithm. The proposed methodology was then applied to spare parts location optimization problems of electronic toll collection (ETC) systems in a real-world expressway network in Japan. The results demonstrate that the proposed methodology significantly reduced the optimization computation time by 85.6% to 97.9% compared with an existing method, showcasing a substantial improvement in computational efficiency while also obtaining near-optimal solutions. • Optimize road facility spare parts depot locations using continuum approximation. • Develop a model for spare parts planning considering expressway facility failures. • Handle network distances by embedding a road network into a new Euclidean space. • Achieve 85.6% to 97.9% reduction in optimization computation time. • Validate robustness of the method via stable performance in sensitivity analysis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".