Optimizing stochastic multi-project scheduling with a simulation integrated multi-objective genetic algorithm
Why this work is in the frame
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Bibliographic record
Abstract
The significance of project scheduling and sequencing has increased considerably in recent years, driven by the rising customer demand for highly personalized solutions. Companies have to consider multiple criteria while executing multiple projects simultaneously to meet the customer demands. Therefore, this study focuses on the multi-objective multi-project scheduling and sequencing problem (MP-SSP). A simulation-based mathematical model is developed and integrated with a multi-objective genetic algorithm. The objectives of this model are to minimize the project execution cost, project completion time and project lateness simultaneously while maximizing the resource utilization in the stochastic environment. Goal attainment programming is introduced in the simulation integrated multi-objective genetic algorithm (SIHMO-GA) to increase the effectiveness of the algorithm. Further, response surface methodology (RSM) has been used to find the optimum parameters of the proposed SIHMO-GA. The effectiveness of the proposed SIHMO-GA is evaluated through a real-world case study by comparing it with simulation-optimization approaches, namely the multi-objective genetic algorithm (MOGA) and goal attainment programming. Gap analysis indicates that the SIHMO-GA provides best trade off values of the above-mentioned conflicting objectives under a stochastic environment. This study supports practical scheduling and sequencing of multiple projects in a stochastic environment by generating solutions that maximize profit, enhance resource utilization, and ensure customer satisfaction through timely project delivery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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