Vehicle routing decision-support system development using integer programming and heuristics: a model-driven structured approach
Bibliographic record
Abstract
In this article, a model-driven structured approach is adopted to develop a decision support system for the capacitated vehicle routing problem. A repository of artefacts is developed through system initiation, analysis, design, and implementation. Data about the problem is gathered, and existing procedures are analysed and improved using key stakeholders' knowledge to maintain continuous communication throughout the stages with involved parties. The DSS adopts mathematical programming and a heuristic to obtain exact and good solutions. The nearest neighbourhood heuristic is employed to solve large instances. IDEF0 and a problem statement are employed for system initiation. A cause-effect analysis is conducted for problem analysis. Use-case diagrams and narratives are used for requirements analysis. Logical and physical data flow diagrams are developed for system design. The system is implemented using Excel internal VBA language and the Application Programming Interfaces for Frontline Solver and Google Maps. Fico Xpress is used for exact solutions.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".