by Ouijdane Guiza, Christoph Mayr-Dorn, Michael Mayrhofer, Alexander Egyed, Heinz Rieger, Frank Brandt
Abstract:
The Assembly Line Balancing Problem (ALBP) is of great relevance for manufacturing companies improving the line efficiency and productivity and thus maximizing production profits. Multiple exact, heuristic and meta-heuristic methods have been applied to solve the ALBP. These optimization methods consist in producing a feasible line balance, i.e. the partitioning of assembly tasks among available work stations based on, among others, the precedence graph. Such a graph describes the technological and organizational precedence constraints between tasks. Unfortunately, the assembly precedence relations, in the automotive and related industries for example, are often outdated, incomplete or altogether unavailable. This limits the applicability of the available approaches to real-world assembly systems. Grounded in an industry use-case, we propose a novel approach for the assistance in the upfront assignment of assembly tasks to stations. We recommend station assignments relying on historical data of prior feasible assembly balances of different products. We evaluate our approach against real industry data. On average, our approach is able to provide station assignment recommendations for 91% of the tasks at 82% precision.
Reference:
Recommending Assembly Work to Station Assignment Based on Historical Data (Ouijdane Guiza, Christoph Mayr-Dorn, Michael Mayrhofer, Alexander Egyed, Heinz Rieger, Frank Brandt), In 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021, Vasteras, Sweden, September 7-10, 2021, IEEE, 2021.
Bibtex Entry:
@Conference{Guiza2021,
author = {Ouijdane Guiza and Christoph Mayr-Dorn and Michael Mayrhofer and Alexander Egyed and Heinz Rieger and Frank Brandt},
booktitle = {26th {IEEE} International Conference on Emerging Technologies and Factory Automation, {ETFA} 2021, Vasteras, Sweden, September 7-10, 2021},
title = {Recommending Assembly Work to Station Assignment Based on Historical Data},
year = {2021},
pages = {1--8},
publisher = {{IEEE}},
abstract = {The Assembly Line Balancing Problem (ALBP) is of great relevance for manufacturing companies improving the line efficiency and productivity and thus maximizing production profits. Multiple exact, heuristic and meta-heuristic methods have been applied to solve the ALBP. These optimization methods consist in producing a feasible line balance, i.e. the partitioning of assembly tasks among available work stations based on, among others, the precedence graph. Such a graph describes the technological and organizational precedence constraints between tasks. Unfortunately, the assembly precedence relations, in the automotive and related industries for example, are often outdated, incomplete or altogether unavailable. This limits the applicability of the available approaches to real-world assembly systems. Grounded in an industry use-case, we propose a novel approach for the assistance in the upfront assignment of assembly tasks to stations. We recommend station assignments relying on historical data of prior feasible assembly balances of different products. We evaluate our approach against real industry data. On average, our approach is able to provide station assignment recommendations for 91% of the tasks at 82% precision.},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/conf/etfa/GuizaMMERB21.bib},
doi = {10.1109/ETFA45728.2021.9613480},
file = {:Conferences/ETFA 2021 - Recommending Assembly Work to Station Assignment Based on Historical Data/Recommending Assembly Work to Station Assignment Based on Historical Data - preprint.pdf:PDF},
keywords = {Pro2Future},
timestamp = {Tue, 07 Dec 2021 09:18:02 +0100},
url = {https://doi.org/10.1109/ETFA45728.2021.9613480},
}