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https://hdl.handle.net/10316/115026
Title: | Collective Intelligence Application in a Kitting Picking Zone of the Automotive Industry | Authors: | Zapata, Santiago Montoya Klement, Nathalie Silva, Cristovão Gibaru, Olivier Lafou, Meriem |
Keywords: | Artificial Intelligence; Collective Intelligence; Multiagent Systems; Kitting Picking; Automotive Industry | Issue Date: | 2023 | Project: | UID/EMS/00285/2020 | Serial title, monograph or event: | Lecture Notes in Mechanical Engineering | Abstract: | The durability of an automobile factory depends on its flexibility and its evolution capacity to meet market expectations. These expectations tend increasingly to the vehicles’ customization. Therefore, automobile factories may be able to manufacture several vehicle models on the same assembly line. It makes automobile manufacturers face big logistic challenges in their production sites. They must be capable of simplifying, synchronizing and proposing intelligent and flexible logistic flow. Thus, digital tools for decision support are needed. This paper aims to propose an architecture to model the logistic process of supplying materials to the assembly line as a multiagent system. Thus, multiagent learning and collective intelligence techniques can be applied to guarantee a good performance of the process. The case study focuses on a kitting picking zone from a Renault production site which manufactures six different vehicle models, each one with its variants. | URI: | https://hdl.handle.net/10316/115026 | ISSN: | 2195-4356 2195-4364 |
DOI: | 10.1007/978-3-031-15928-2_36 | Rights: | openAccess |
Appears in Collections: | FCTUC Eng.Mecânica - Artigos em Revistas Internacionais I&D CEMMPRE - Artigos em Revistas Internacionais |
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Collective Intelligence Application in a Kitting Picking Zone of the Automotive Industry.pdf | 503.17 kB | Adobe PDF | View/Open |
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