Activity Sequencing Optimization in Petroleum Projects Using Simulation Modeling
Why this work is in the frame
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Bibliographic record
Abstract
Project management benefits from mathematical models that enhance resource allocation, scheduling, and cost efficiency while managing uncertainties. Although optimization is well-studied in construction, its use in sequencing petroleum project activities remains unexplored. This study develops an integrated simulation and optimization model to refine scheduling in refinery upgrades, minimizing project duration and addressing operational complexities. This paper presents a simulation-based optimization model designed to improve scheduling efficiency in a refinery upgrade project, where multiple tasks must be executed concurrently without extending the overall project duration. The model accounts for interdependencies among activities and resource requirements across internal and external work teams, ensuring optimal coordination and utilization. Developed using AnyLogic®, the simulation framework employs a random number generator to systematically explore task sequencing variations, leading to a refined execution strategy. The optimization results indicate a 20% reduction in the project's total duration. While resource utilization was assessed, it was not the model's primary objective. The utilization of resources has shown mixed outcomes; specific resources demonstrated an improvement of nearly 50%, yet the overall average utilization significantly decreased to just 0.12%, falling below the typical baseline of 40% observed in most resources. The model's performance and the optimization outcomes are analyzed, offering a decision-support tool for complex project management scenarios.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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