1. Αποθετήριο Παραδοτέων Υποτρόφων Δράσεων ΕΣΠΑ Τριτοβάθμιας Εκπαίδευσης 2017-2023
2. Όλο το Αποθετήριο
3. Υποψήφιοι διδάκτορες

Τύπος:	Διδακτορική διατριβή
Τίτλος:	Hybrid machine learning and deep learning methodologies for resilient, sustainable and interactive cyberphysical manufacturing systems: Joint production, maintenance, and quality control optimization
Εναλλακτικός τίτλος:	Υβριδικές μέθοδοι μηχανικής και βαθιάς μάθησης για ανθεκτικά, βιώσιμα και αλληλεπιδραστικά κυβερνοφυσικά συστήματα παραγωγής: συνδυαστική βελτιστοποίηση παραγωγής, συντήρησης και ποιότητας
Συγγραφέας:	[EL] Παράσχος, Παναγιώτης[EN] Paraschos, Panagiotis
Επιβλέπων διατριβής:	[EL] Κουλουριώτης, Δημήτριος[EN] Koulouriotis, Dimitrios
Συμβουλευτική επιτροπή:	[EL] Βαβάτσικος, Αθανάσιος[EN] Vavatsikos, Athanasios [EL] Παπαντωνόπουλος Σωτήριος[EN] Papantonopoulos, Sotiris
Μέλος εξεταστικής επιτροπής:	[EL] Γαστεράτος, Αντώνιος[EN] Gasteratos, Antonios [EL] Ξανθόπουλος, Αλέξανδρος[EN] Xanthopoulos, Alexandros [EL] Κουλίνας, Γεώργιος[EN] Koulinas, Georgios [EL] Τσουρβελούδης, Νικόλαος[EN] Tsourveloudis, Nikos
Ημερομηνία:	23/09/2023
Περίληψη:	In manufacturing, advances in Artificial Intelligence led to the introduction of novel paradigms, e.g., Industry 4.0 and smart manufacturing, that endeavor to digitize processes and operations of plants. This digital transformation is achieved through the integration of intelligent technologies that facilitate the decision-making of manufacturers. Such technologies are the cyberphysical systems, which attempt to create a “bridge” between the computing process and the physical manufacturing environments through their integrated sensor devices. In this regard, they interchange information with their environment and collect feedback from it. To adapt themselves and carry out decision-making, the cyberphysical systems process that feedback using artificial intelligence methodologies, including machine learning and deep learning. As a result, useful knowledge regarding manufacturing components and aspects, such equipment condition and inventory level, could be extracted in an effort to aid decision-makers in devising effective schedules and plans for manufacturing activities. Toward this end, the present dissertation studies the joint control and planning problem within cyberphysical manufacturing systems. The aim is the realization of sustainable, resilient, and interactive cyberphysical manufacturing systems. With that aim in mind, the dissertation proposes a variety of hybrid machine learning and deep learning methodologies. These methodologies are incorporated into decision-making frameworks that strive to derive optimal, or near optimal joint control policies for production, maintenance, and quality control activities in the context of stochastic and deteriorating production environments, involving single, or multiple processing machines and storage facilities. The behavior of these environments is specifically defined by a number of random events. Among these events, failures deteriorate the condition of the considered systems. The quality of the produced parts is affected as well by the deteriorated system condition. As such, the proposed frameworks are applied to a diversity of manufacturing environments as follows. The first one follows reinforcement learning decision-making for the joint optimization of manufacturing, maintenance, and recycling operations in a single-stage production/inventory system, which produces and stockpiles a single-type of products. Afterwards, the behavior and functionality of this framework is expanded for the joint control of alternative production environments. In this respect, hybrid reinforcement learning methodologies are introduced to optimize circular manufacturing systems. In contrast to other production systems, the circular manufacturing ones attempt to produce new products by reusing low-quality, or returned ones in an effort to reduce the generation of waste. Given that, the proposed methodologies involve either one, or two reinforcement learning agents to formulate strategies for the processing machines included in the studied systems. Their decision-making integrates well-known ad-hoc control policies. The incorporated policies include pull production policies, e.g., Kanban, and condition-based maintenance. The aim of this implementation is the minimization of redundant costs related with unsold items and unnecessary authorization of activities. Due to its promising performance, the two-agent hybrid reinforcement learning approach is also applied for addressing sustainable manufacturing. Within this context, green and sustainable practices are considered for reforming low-quality material. The final methodology involves a predictive model constructed by means of deep learning. This model strives to devise activity authorizations by identify fluctuating aspects of manufacturing systems, e.g., inventory level. Lastly, the evaluation of the proposed hybrid machine learning and deep learning methodologies is performed through a series of experiments simulating the behavior of the studied manufacturing systems. Findings of this evaluation validate the performance and efficiency of introduced methodologies in terms of cost-efficiency, sustainability, material management, and system resilience.
Γλώσσα:	Αγγλικά
Τόπος δημοσίευσης:	Ξάνθη, Ελλάδα
Σελίδες:	149
Θεματική κατηγορία:	[EL] Οργάνωση παραγωγής και Μηχανική των κατεργασιών[EN] Industrial and Manufacturing Engineering
Λέξεις-κλειδιά:	intelligent multi-agent systems; circular economy; green lean manufacturing; deep neural network; learning-based scheduling and control
Κάτοχος πνευματικών δικαιωμάτων:	© Παράσχος Παναγιώτης
Όροι και προϋποθέσεις δικαιωμάτων:	CC-BY-NC-ND
Διατίθεται ανοιχτά στην τοποθεσία:	https://drive.google.com/file/d/1VToqT6PaevuwnW58DlMGZWfUm86rC8TH/view?usp=drive_link
Σημειώσεις:	«Η υλοποίηση της διδακτορικής διατριβής συγχρηματοδοτήθηκε από την Ελλάδα και την Ευρωπαϊκή Ένωση (Ευρωπαϊκό Κοινωνικό Ταμείο) μέσω του Επιχειρησιακού Προγράμματος «Ανάπτυξη Ανθρώπινου Δυναμικού, Εκπαίδευση και Δια Βίου Μάθηση», 2014-2020, στο πλαίσιο της Πράξης «Ενίσχυση του ανθρώπινου δυναμικού μέσω της υλοποίησης διδακτορικής έρευνας Υποδράση 2: Πρόγραμμα χορήγησης υποτροφιών ΙΚΥ σε υποψηφίους διδάκτορες των ΑΕΙ της Ελλάδας». ≪The implementation of the doctoral thesis was co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme ≪Human Resources Development, Education and Lifelong Learning≫ in the context of the Act ”Enhancing Human Resources Research Potential by undertaking a Doctoral Research” Sub-action 2: IKY Scholarship Programme for PhD candidates in the Greek Universities≫.
Εμφανίζεται στις συλλογές:	Υποψήφιοι διδάκτορες