| Titre : |
Data-driven optimization for nurse scheduling and rescheduling problem |
| Type de document : |
document électronique |
| Auteurs : |
Hadil Chorfi, Auteur ; Samia Beldjoudi, Directeur de thèse ; Yassine Ouazene, Directeur de thèse ; Yasmine Alaouchiche, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2025 |
| Importance : |
1 fichier PDF (2.3 Mo) |
| Présentation : |
ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Génie Industriel.Management industriel : Alger, École Nationale Polytechnique : 2025
Bibliogr. p. 82 - 86 .- Annexe p. 87 - 97 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Nurse scheduling
Rescheduling
Heuristic
Absence
Hurdle model
Predictive modeling
Multi-objective programming |
| Index. décimale : |
PI01925 |
| Résumé : |
Nurse scheduling in hospitals is a highly constrained and uncertain task. While traditional optimization models can generate feasible baseline schedules, they often fail to account for unplanned disruptions such as last-minute absences. These absences compromise care quality, create workload imbalances, and force costly last-minute adjustments. Existing models rarely integrate predictive insights or proactive mechanisms to handle such volatility. The core challenge addressed in this work is to design a scheduling and rescheduling system that anticipates and reacts to daily absences with minimal disruption, while maintaining fairness, regulatory compliance, and staffing quality. |
Data-driven optimization for nurse scheduling and rescheduling problem [document électronique] / Hadil Chorfi, Auteur ; Samia Beldjoudi, Directeur de thèse ; Yassine Ouazene, Directeur de thèse ; Yasmine Alaouchiche, Directeur de thèse . - [S.l.] : [s.n.], 2025 . - 1 fichier PDF (2.3 Mo) : ill. Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Génie Industriel.Management industriel : Alger, École Nationale Polytechnique : 2025
Bibliogr. p. 82 - 86 .- Annexe p. 87 - 97 Langues : Anglais ( eng)
| Mots-clés : |
Nurse scheduling
Rescheduling
Heuristic
Absence
Hurdle model
Predictive modeling
Multi-objective programming |
| Index. décimale : |
PI01925 |
| Résumé : |
Nurse scheduling in hospitals is a highly constrained and uncertain task. While traditional optimization models can generate feasible baseline schedules, they often fail to account for unplanned disruptions such as last-minute absences. These absences compromise care quality, create workload imbalances, and force costly last-minute adjustments. Existing models rarely integrate predictive insights or proactive mechanisms to handle such volatility. The core challenge addressed in this work is to design a scheduling and rescheduling system that anticipates and reacts to daily absences with minimal disruption, while maintaining fairness, regulatory compliance, and staffing quality. |
|
PI00100 
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