Evaluation and ranking of multi-type projects with mixed multi-criteria cost/benefit and optimization of project portfolio selection
Bibliographic record
Abstract
A majority of companies are involved in the planning and execution of projects. The number of projects that companies need to evaluate has significantly increased in recent years. This trend has various causes, such as the digitalization of corporate processes, diversification, or strategic positioning in the face of ever-changing market conditions. The characterization of projects into mandatory and optional, as well as the evaluation of these projects, can be conducted based on various mixed criteria, which may include both cost and benefit criteria. The limited resources of companies necessitate a critical assessment of projects. They must be ranked based on realistic and plausible criteria regarding their benefits and objectives of the company. In the literature, various approaches to project evaluation exist. Examples include financial assessment, the utilization of evaluation models considering risks, or even multi-criteria models that incorporate different aspects of projects into the evaluation process. We propose a robust, scalable, and easily calibrated multi-criteria evaluation model for project evaluation and ranking, encompassing evaluation criteria such as financial criteria measured by Net Present Value (NPV), Risk, Classification, Priority, Strategy, and Sustainability. To achieve this goal, the Technique for Order of Preference by Similarity to Ideal Solution with multi type projects multi mixed cost and benefit criteria (TOPSIS-MTPMMCBC) is employed. The model is adapted to evaluate and rank optional and mandatory projects. An important feature of the projects in this study is that the criteria values of the projects can have negative or positive values. Particularly noteworthy is the increasing significance of sustainability as a key criterion for businesses, driven by political mandates. Consequently, a decision based on the criterion of sustainability will be important in the future and is implemented in the proposed model. The proposed research can be adapted to use a variety of Key Performance Indicators (KPIs) as multiple decision criteria. An objective calculation of the criteria weights for sample dataset was carried out using the CRITIC method, followed by a sensitivity analysis. Subsequently, the optimization of the project portfolio was carried out by combining an integer programming model with the proposed TOPSIS-MTPMMCBC method. An initial solution of project evaluation and ranking is conducted to demonstrate the applications.
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How this classification was reachedexpand
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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".