An assembly line is a flow-oriented production system where the productive units performing the operations, referred to as stations, are aligned in a serial manner. The work pieces visit stations successively as they are moved along the line usually by some kind of transportation system, e.g., a conveyor belt. An important decision problem, called Assembly Line Balancing Problem (ALBP), arises and has to be solved when (re-) configuring an assembly line. It consists of distributing the total workload for manufacturing any unit of the product to be assembled among the work stations along the line. The assignment of tasks to stations is constrained by task sequence restrictions which can be expressed in a precedence graph. However, most manufacturers usually do not have precedence graphs or if they do, the information on their precedence graphs is inadequate. 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 detailed application of a new graph generation approach first introduced by Klindworth et al. [1] that is based on learning from past feasible production sequences. This technique forms a sufficient precedence graph that guarantees feasible line balances. Experiments indicate that the proposed procedure is able to approximate the real precedence graph sufficiently well to detect nearly optimal solutions even for a real-world automotive assembly line segment with up to 317 tasks. In particular, it seems to be promising to use interviews with experts in a selective manner by analyzing maximum and minimum graphs to identify still assumed relations that are crucial for the graph’s structure. 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.
