Evaluation and ranking of multi-type projects with mixed multi-criteria cost/benefit and optimization of project portfolio selection
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».