Smart and sustainable flow-shop scheduling problems: Scenario-based robust optimization and strong heuristics
Notice bibliographique
Résumé
This Ph.D. thesis is dedicated to the development of a smart and sustainable approach to the Distributed Permutation Flow Shop Scheduling Problem (DPFSP) through the utilization of practical optimization models, efficient reformulations, heuristics, and advanced metaheuristics. The DPFSP is an extension of the Permutation Flow Shop Scheduling Problem (PFSP) and serves as its foundational model. The key distinction between the DPFSP and the PFSP lies in their respective scheduling scopes. While the PFSP focuses on scheduling tasks within a single plant, the DPFSP addresses the more complex challenge of scheduling tasks across multiple distributed factories. \n \nWhile prior research has made contributions to the field of DPFSP, this Ph.D. project stands out by incorporating the concepts of sustainability, real-time scheduling, and scenario-based robust optimization into the DPFSP framework. The primary objective of this research is to integrate environmental and social criteria based on the Triple Bottom Line (TBL) to meet the guidelines of the Sustainable Development Goals (SDGs). By considering criteria such as energy consumption, job opportunities, and lost workdays, a multi-objective optimization model and an efficient multi-objective metaheuristic algorithm are developed. \n \nAnother critical research gap in the field of production scheduling involves the intelligent collection, analysis, and conversion of data into actionable information using real-time decision-making strategies for production systems. In response to this grand challenge, the second objective of this Ph.D. project is to address the uncertainty in the DPFSP by modeling it within the real-time optimization framework of Industry 4.0. A real-time optimization approach is proposed to handle task reassignment to machines under uncertain process times, new task arrivals, or planned machine breakdowns. By incorporating the concepts of Industry 4.0, a comprehensive optimization model using different manual and automated modes of production is proposed and various real-time scheduling strategies and policies are examined into this model. For solving it, constructive heuristics, Lagrangian relaxation and Benders decomposition reformulations are studied. \n \nWhile the second objective addresses uncertainty to some extent, the third objective utilizes a scenario-based robust optimization approach to efficiently address uncertainty in the DPFSP by considering all possible scenarios. The final objective of this Ph.D. project is to address the challenges of the smart and sustainable DPFSP through the development of a comprehensive optimization framework. This framework combines a scenario-based robust optimization model and an advanced metaheuristic algorithm based on adaptive large neighborhood search (ALNS) using various heuristic and local search algorithms. By employing a scenario-based robust optimization approach, the framework considers a range of possible scenarios that may arise due to various disruptions in production schedules. These disruptions can include machine breakdowns, arrival of new tasks, or variations in task processing times. By incorporating these uncertainties into the optimization process, the framework enables the identification of schedules that are robust and resilient to unforeseen circumstances. \n \nOverall, this Ph.D. project represents a significant advancement in the field of DPFSP by leveraging the principles of sustainability, real-time scheduling, and robust optimization. Through the application of practical optimization models, efficient reformulations, heuristics, and metaheuristics, this research aims to address the unique challenges posed by scheduling tasks across distributed factories. By contributing to the development of smarter and more sustainable production systems, this work has far-reaching implications for the field and industry as a whole.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».