Please use this identifier to cite or link to this item:
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: CIFRE PhD granted by LISPEN Laboratory from ENSAM and IF&A department from Renault Group 
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.
ISSN: 2195-4356
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

Show full item record

Page view(s)

checked on May 22, 2024


checked on May 22, 2024

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.