Computerized System for Efficient Scheduling of Highway Construction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A practical model for scheduling and cost optimization of highway construction is presented in this paper. The model's objective is to minimize total construction cost while respecting the time and resource constraints of a project. For flexibility, the model allows each activity to have three alternative construction methods, from cheap and slow to fast and expensive. At the core of the model is an innovative scheduling algorithm for crew assignments along the various highway sections so that work continuity is maintained. The model is designed to be flexible enough to allow nontypical sections as well as variable sequence of work among the sections. For cost optimization, the model uses a nontraditional optimization technique, genetic algorithms. Details of the model formulation are presented in this paper along with its implementation in a simple-to-use computer system. To demonstrate the capabilities of the system, a realistic example is given of a highway project that is fast-tracked by using two crews working simultaneously from opposite sides. The example shows the usefulness of the system for what-if analysis, schedule optimization, and legible presentation of detailed crew assignments and activities’ progress speeds. Extensions of the model to incorporate project control features are then highlighted.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it