Enhanced repetitive scheduling formulation for meeting deadlines and resource constraints in linear and scattered projects
Bibliographic record
Abstract
The majority of civil infrastructure projects involve activities that are repeated across a number of linear units (e.g., highway sections) or scattered units (e.g., multi-bridge rehabilitations). For these projects, Critical Path Method (CPM)/line-of-balance (LOB) repetitive scheduling combines the benefits of CPM and LOB analyses to maintain work continuity across units, yet assumes simple sequential unit order and results in schedule delays for practical projects with non-identical units, constrained resources, and (or) strict deadlines. To improve CPM/LOB scheduling, this paper introduces powerful and easy-to-use enhancements, including (1) designed interruptions to reduce time gaps; (2) efficient resource-constrained first-come first-serve crew assignment; (3) crew adjustment loop to meet deadlines; and (4) representation of flexible unit sequence. These simplified enhancements computationally produce schedules that respect deadlines, individual resource limits, and desired sequence among units. Example projects are then presented to prove that the proposed enhancements offer flexible scheduling features that can meet the strict constraints of infrastructure projects.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".