Agent-Based Simulation of Multi-Crew Allocation to Scattered Repetitive Projects
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
Scattered Repetitive Projects (SRPs) such as multi-school or multi-bridge rehabilitations are rising in numbers, complexity, and costs. To properly allocate resources for these complex projects, efficient planning and simulation become necessary. At the detailed level, Agent-Based Modeling and Simulation (ABMS) is among the powerful techniques that can be used to analyze the impact of crew movements and behaviors on task productivity. To support efficient allocation of crews to SRPs, this research developed an ABMS model using the AnyLogic software to simulate multi-crew allocation to scattered units, incorporating crews’ travel times among the units using GIS. The paper discusses the model and its implementation on a case study of scattered linear projects where the activities in each location are sequential. Model validation against a powerful schedule optimization model proved its flexibility and applicability. Future integration with a powerful repetitive scheduling engine is highlighted to consider more complex networks at different locations.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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