Simulation tool for manpower forecast loading and resource leveling
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
Large scale/mega projects are lengthy complex endeavors that require significant planning by management, engineers and construction personnel to ensure the success of the project. When we examine the state of mega projects today, we are faced with a real crisis. Companies, both client and contractor, are reporting significant cost and schedule overruns. Lack of project scope definition and planning are the primary characteristics of this problem. Computer simulation is a powerful tool for analyzing complex and dynamic scenarios. It provides an appealing approach for the analysis of repetitive processes. Simulation helps decision makers identify different possible options by analyzing enormous amounts of data. Hence, computer simulation can be used effectively to analyze the resource loading and manpower requirements needed to complete a task in a given time frame, based on current progress levels. This paper discusses a special- purpose simulation (SPS) tool for optimization of manpower forecast loading and resource leveling. The simulation model is capable of optimizing resource requirements for a petrochemical project, based on standard discipline requirements and involvements. Tests of this simulation tool have produced exceptional results; currently, the system is being modified to incorporate historical data within the simulation.
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 | 0.001 | 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