Multiobjective bilevel optimization for transportation planning and management problems
Bibliographic record
Abstract
Abstract Many previous studies have formulated the decision‐making problems in transportation system planning and management as single‐objective bilevel optimization models. However, real‐world decision‐making processes always have several social concerns and thus multiple objectives need to be achieved simultaneously. In most cases, these objective functions conflict with each other and are also not simple enough to be combined into a single one. Therefore it is necessary to apply multiobjective optimization to generate non‐dominated or Pareto optimal alternatives. It can be foreseen that the multiobjective bilevel modeling approach can become a powerful, and possibly interactive, decision tool, allowing the decision‐makers to learn more about the problem before committing to a final decision. Such multiobjective bilevel models are difficult to solve due to their intrinsic nonconvexity and multiple objectives. This paper consequently proposes a solution algorithm for the multiobjective bilevel models using genetic algorithms. The proposed algorithm is illustrated, using the numerical example taken from the previous study. It is found that the proposed algorithm is efficient to search simultaneously the Pareto optimal solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".