Activity Sequencing Optimization in Petroleum Projects Using Simulation Modeling
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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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) | 0.001 | 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".