Multiple-source learning precedence graph concept for the automotive industry

Christian Otto and Alena Otto

European Journal of Operational Research, 2014, vol. 234, issue 1, 253-265

Abstract: In modern production systems, customized mass production of complex products, such as automotive or white goods, is often realized at assembly lines with a high degree of manual labor. For firms that apply assembly systems, the assembly line balancing problem (ALBP) arises, which is to assign optimally tasks to stations or workers with respect to some constraints and objectives. Although the literature provides a number of relevant models and efficient solution methods for ALBP, firms, in most cases, do not use this knowledge to balance their lines. Instead, the planning is mostly performed manually by numerous planners responsible for small sub-problems. This is because of the lack of data, like the precedence relations between the tasks to be performed. Such data is hard to collect and to maintain updated.

Keywords: Combinatorial optimization; Assembly line balancing; Incomplete precedence graph; Learning approach; Decision support (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:234:y:2014:i:1:p:253-265

DOI: 10.1016/j.ejor.2013.09.034

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