Heuristics for solving a multi model robotic assembly line balancing problem.pdf (1.51 MB)
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Heuristics for solving a multi-model robotic assembly line balancing problem

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journal contribution
posted on 12.09.2019, 13:42 by Mads Kammer Christensen, Mukund Nilakantan Janardhanan, Peter Nielsen
Topic of balancing assembly lines is of great interest for researchers and industry practitioners due to the significant impact it has on increasing productivity and efficiency of manufacturing systems. Robots are widely applied in manufacturing industries for assembly processes. Wide literature has been reported on balancing of robotic assembly lines with single and mixed models. Researchers have extensively used heuristics and metaheuristics to solve these problems due to their NP-hard nature. However, no work has been reported on how to balance a robotic assembly line with multiple models (MuRALB) with batch production. This problem is highly relevant for large-scale assembly of products found, e.g. the automotive industry. To authors’ knowledge, this is the first attempt to solve this problem. This research proposes a novel heuristic to solve type II MuRALB problem. Type II problem deals with minimizing the cycle time for a fixed set of robots. Heuristic is implemented, and method for scheduling batched production with related setup times for a robotic assembly line is presented, and based on the analysis conducted, advantage of batching is presented. Proposed heuristic is tested on a set of new five datasets, and performance of this heuristic and batching is presented in detail.

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Citation

Production and Manufacturing Research, 2017, 5 (1), pp. 410-424

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Engineering

Version

VoR (Version of Record)

Published in

Production and Manufacturing Research

Publisher

Taylor & Francis

eissn

2169-3277

Acceptance date

06/11/2017

Copyright date

2017

Available date

12/09/2019

Publisher version

https://www.tandfonline.com/doi/full/10.1080/21693277.2017.1403977

Language

en

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