An enhanced multi-objective optimization approach for risk allocation in public–private partnership projects: a case study of Malaysia
Bibliographic record
Abstract
The decision making for risk allocation problems in public–private partnership (PPP) projects is a vital process that directly affects the timeliness, cost, and quality of the project. Fair risk allocation is a vital factor to achieve success in the implementation of these projects. It is essential for private and public sectors to apply efficient risk allocation approaches to experience a more effective process of agreement arbitration and to reduce the appearance of dispute during the concession period. The aim of this study is to develop an optimization approach to enhance risk allocation process in PPP projects. The shared risks in projects are identified through comprehensive literature review and questionnaire survey obtained from Malaysian professionals involved in PPP projects. Objective functions are then developed to minimize the total time and cost of the project and maximize the quality while satisfying risk threshold constraints. The combinatorial nature of the risk allocation problem describes a multi-objective situation that can be simulated as a knapsack problem (KP). The formulation of the KP is described and solved applying genetic algorithm (GA). Due to the flexibility of GA, the results are Pareto Optimal solutions that describe the combinations of risk percentages for shared risks in PPP projects.
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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 | 0.003 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".