A Hybrid of Genetic Algorithm and Evidential Reasoning for Optimal Design of Project Scheduling: A Systematic Negotiation Framework for Multiple Decision-Makers
Bibliographic record
Abstract
Traditional project scheduling methods inherently assume that the decision makers (DMs) are a unique entity whose acts are based on group rationality. However, in practice, DMs’ reliance on individual rationality and the wish to optimize their own objectives skew negotiations towards their preferred solutions. This makes conventional project scheduling solutions unrealistic. Here, a new two-step method is proposed that seeks to increase the overall efficiency of project schedules without violating individual rationality criteria, to find scheduling solutions that are acceptable to all DMs. First, a genetic algorithm is combined with evidential reasoning (ER) to obtain near optimal project schedule alternatives with respect to the priorities of each DM, separately. Second, the fallback bargaining method is used to help the DMs reach a consensus on an alternative with the highest group satisfaction. The proposed model is tested on a benchmark project scheduling problem with over 3.6 billion possible project scheduling alternatives. The results show that the model helps DMs when appointing their preferences using a well-organized procedure to provide a transparent view of each project schedule performance solution. Furthermore, the model is able to absorb the maximum support from the DMs, not necessarily a unique entity, by collecting all the self-optimizing DMs’ preferences and fairly allocating the benefits.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".