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Scheduling for a hybrid flow shop system with distributed additive manufacturing and post-processing factories

Sara Aubert Pagliero

Scheduling for a hybrid flow shop system with distributed additive manufacturing and post-processing factories.

Rel. Erica Pastore, Manuela Galati. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management), 2023

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Additive manufacturing (AM) has achieved significant importance in recent years as a rapidly growing and promising fabrication process. This technology overcomes traditional manufacturing by efficiently producing unique and complex parts and enabling cost-effective mass customization. However, in order to enable mass production, there are several challenges that must be faced. One particular difficulty is the scheduling of additive manufacturing jobs, as it involves producing multiple heterogeneous jobs together in a batch. The aim of this thesis is to partially address this issue by focusing on the optimization of the makespan of a hybrid flow shop system comprising parallel identical distributed additive manufacturing factories, and the posterior post-processing facilities. A Mixed Integer Linear Programming (MILP) mathematical model was developed to properly formulate the scheduling problem, involving three stages. These are the additive manufacturing and post-processing phase 1 stages, in which jobs are produced in non-preemptive batches, and both are implemented in additive manufacturing factories. Additionally, the other stage is the post-processing phase 2, which is carried out in parallel, identical, and distributed post-processing factories. As the problem is NP-hard, in order to find a near-optimal schedule, a genetic algorithm is generated using the mathematical model to find the fitness function. Finally, a case study, considering different scenarios with diverse numbers of additive manufacturing factories, each one with a single Electron Beam Powder Bed Fusion (EB-PBF) machine, and post-processing factories was presented. Multiple iterations of the genetic algorithm were conducted for each scenario, aiming to identify a near-optimal makespan. This experimentation was carried out to validate the algorithm's feasibility and efficacy, demonstrating its practicality and value in solving the system under analysis.

Relators: Erica Pastore, Manuela Galati
Academic year: 2022/23
Publication type: Electronic
Number of Pages: 142
Corso di laurea: Corso di laurea magistrale in Ingegneria Gestionale (Engineering And Management)
Classe di laurea: New organization > Master science > LM-31 - MANAGEMENT ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/27439
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