The car sequencing (CS) problem seeks a production sequence of different car models launched down a mixed-model assembly line. The models can be distinguished by selected options, e.g., sun roof yes/no. For every option, CS applies a so-called sequencing rule to avoid that consecutive models requiring this option lead to a work overload of the respective assembly operators. The aim is to find a sequence with minimum number of sequencing rule violations. This paper presents a graph representation of the problem and develops an exact solution approach based on iterative beam search. Furthermore, existing lower bounds are improved and applied. The experimental results reveal, that our solution approach is superior compared to the currently best known exact solution procedure. Our algorithm can even be applied as an efficient heuristic on problems of real-world size with up to 400 cars, where it shows competitive results compared to the current best known solutions.
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This paper presents novel approaches for generating sequencing rules for the car sequencing (CS) problem in cases of two and multiple processing times per station. The CS problem decides on the succession of different car models launched down a mixed-model assembly line. It aims to avoid work overloads at the stations of the line by applying so-called sequencing rules, which restrict the maximum occurrence of labor-intensive options in a subsequence of a certain length. Thus to successfully avoid work overloads, suitable sequencing rules are essential. The paper shows that the only existing rule generation approach leads to sequencing rules which misclassify feasible sequences. We present a novel procedure which overcomes this drawback by generating multiple sequencing rules. Then, it is shown how to apply both procedures in case of multiple processing times per station. For both cases analytical and empirical results are derived to compare classification quality.Mixed-model assembly lines, Car sequencing, Sequencing rules
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