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A case study at the Nissan Barcelona factory to minimize the ergonomic risk and its standard deviation in a mixed-model assembly line

A case study at the Nissan Barcelona factory to minimize the ergonomic risk and its standard... This work examines a balancing problem wherein the objective is to minimize both the ergonomic risk dispersion between the set of workstations of a mixed-model assembly line and the risk level of the workstation with the greatest ergonomic factor. A greedy randomized adaptive search procedure (GRASP) procedure is proposed to achieve these two objectives simultaneously. This new procedure is compared against two mixed integer linear programs: the MILP-1 model that minimizes the maximum ergonomic risk of the assembly line and the MILP-2 model that minimizes the average deviation from ergonomic risks of the set of workstations on the line. The results from the case study based on the automotive sector indicate that the proposed GRASP procedure is a very competitive and promising tool for further research. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Progress in Artificial Intelligence Springer Journals

A case study at the Nissan Barcelona factory to minimize the ergonomic risk and its standard deviation in a mixed-model assembly line

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Computer Science; Data Mining and Knowledge Discovery; Artificial Intelligence (incl. Robotics); Computer Imaging, Vision, Pattern Recognition and Graphics; Language Translation and Linguistics; Computational Intelligence; Control, Robotics, Mechatronics
ISSN
2192-6352
eISSN
2192-6360
DOI
10.1007/s13748-018-0153-9
Publisher site
See Article on Publisher Site

Abstract

This work examines a balancing problem wherein the objective is to minimize both the ergonomic risk dispersion between the set of workstations of a mixed-model assembly line and the risk level of the workstation with the greatest ergonomic factor. A greedy randomized adaptive search procedure (GRASP) procedure is proposed to achieve these two objectives simultaneously. This new procedure is compared against two mixed integer linear programs: the MILP-1 model that minimizes the maximum ergonomic risk of the assembly line and the MILP-2 model that minimizes the average deviation from ergonomic risks of the set of workstations on the line. The results from the case study based on the automotive sector indicate that the proposed GRASP procedure is a very competitive and promising tool for further research.

Journal

Progress in Artificial Intelligence – Springer Journals

Published: Jun 5, 2018

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