Sequencing and Scheduling Optimization in Low-Volume Low-Variety Production Systems
Notice bibliographique
Résumé
<p>Despite prominent scholarly advancements in scheduling optimization approaches for a wide range of production systems, limited research has been reported on sequencing and scheduling optimization strategies in Low-Volume Low-Variety production systems. This dissertation fills the gap in the current literature through the formulation and the proposal of a suite of mathematical programming models and heuristic algorithms, capturing the unique characteristics and constraints inherent in such production systems. In the first section of this dissertation a suite of mixed-integer multi-objective linear mathematical programming models are proposed for solving discrete-time single work center scheduling problems, distinguished by a key decision criterion of permitting or prohibiting the traveling of incomplete activities. It was evident through personal observations however, that there exist scenarios where resources are shared between parallel work centers, which yielded to further research in the use of shared resource pools in multi-parallel work center scheduling problems. A novel suite of mathematical programming models are proposed for solving single and multi-parallel work center scheduling problems with shared or dedicated resources. The mathematical programming models formulated in this section are modular, signifying that constraints can be added or removed without jeopardizing the integrity of the mathematical models. The proposed optimization models were validated and verified through a real-world case study where significant cost savings in form of resource requirements are realized through the integration of shared resource pools. It is often the case however, that activity processing times and planning horizon are not discrete. To tackle continuous-time work center scheduling problems a novel suite of mathematical programming models is formulated and proposed in the final section of this dissertation, as well as two new genetic algorithms for solving large-scale scheduling problems. The proposed mathematical programming models and metaheuristics are aimed at optimizing the production schedule as well as activity execution sequence to minimize overall cost and resource requirements. The optimization models proposed through this dissertation are validated and verified through a real-world case study of the final assembly line of a narrow body private aircraft, where the problems were solved to optimality.</p>
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».