Efficient production scheduling is important for both maximizing productivity and minimizing costs in manufacturing environments. This thesis presents an approach to optimizing production scheduling using Artificial Intelligence (AI) and Genetic Algorithms (GAs). The primary goal is to develop a generalized solution that can be modified and adapted to the varying needs that different production- or manufacturing lines may have. This research has two main research questions that address the problem at hand. (1) Can Genetic Algorithms be used to optimize a sequence of products in a production line? and (2) How effectively can Genetic Algorithms optimize the sequencing of production tasks in diverse production lines to minimize total order processing times? Through experimentation with various GA configurations the results achieved suggested that GAs were appropriate for sequence optimization. The study demonstrates that GAs can optimize a production line up to almost 42% , which significantly reduced the total processing time. The thesis also highlights the importance of the representation of data, which is essential for the optimization of the sequence.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mdh-67451 |
| Date | January 2024 |
| Creators | Alander, Aron, Hjalmarsson, Jonathan |
| Publisher | Mälardalens universitet, Akademin för innovation, design och teknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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