id: 06066780
dt: j
an: 06066780
au: Klindworth, Hanne; Otto, Christian; Scholl, Armin
ti: On a learning precedence graph concept for the automotive industry.
so: Eur. J. Oper. Res. 217, No. 2, 259-269 (2012).
py: 2012
pu: Elsevier Science B.V.(North-Holland), Amsterdam
la: EN
cc: 
ut: assembly line balancing; precedence graph; learning approach; production
    process; decision support
ci: 
li: doi:10.1016/j.ejor.2011.09.024
ab: Summary: Assembly line balancing problems (ALBP) consist in assigning the
    total workload for manufacturing a product to stations of an assembly
    line as typically applied in automotive industry. The assignment of
    tasks to stations is due to restrictions which can be expressed in a
    precedence graph. However, (automotive) manufacturers usually do not
    have sufficient information on their precedence graphs. As a
    consequence, the elaborate solution procedures for different versions
    of ALBP developed by more than 50 years of intensive research are often
    not applicable in practice. Unfortunately, the known approaches for
    precedence graph generation are not suitable for the conditions in the
    automotive industry. Therefore, we describe a new graph generation
    approach that is based on learning from past feasible production
    sequences and forms a sufficient precedence graph that guarantees
    feasible line balances. Computational experiments indicate that the
    proposed procedure is able to approximate the real precedence graph
    sufficiently well to detect optimal or nearly optimal solutions for a
    well-known benchmark data set. Even for additional large instances with
    up to 1,000 tasks, considerable improvements of line balances are
    possible. Thus, the new approach seems to be a major step to close the
    gap between theoretical line balancing research and practice of
    assembly line planning.
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