9 research outputs found
The Car Resequencing Problem with Pull-Off Tables
Get PDFThe car sequencing problem determines sequences of different car models launched down a mixed-model assembly line. To avoid work overloads of workforce, car sequencing restricts the maximum occurrence of labor-intensive options, e.g., a sunroof, by applying sequencing rules. We consider this problem in a resequencing context, where a given number of buffers (denoted as pull-off tables) is available for rearranging a stirred sequence. The problem is formalized and suited solution procedures are developed. A lower bound and a dominance rule are introduced which both reduce the running time of our graph approach. Finally, a real-world resequencing setting is investigated.mixed-model assembly line, car sequencing, resequencing
The Car Resequencing Problem with Pull-Off Tables
No full textThe car sequencing problem determines sequences of different car models launched down a mixed-model assembly line. To avoid work overloads of workforce, car sequencing restricts the maximum occurrence of labor-intensive options, e.g., a sunroof, by applying sequencing rules. We consider this problem in a resequencing context, where a given number of buffers (denoted as pull-off tables) is available for rearranging a stirred sequence. The problem is formalized and suited solution procedures are developed. A lower bound and a dominance rule are introduced which both reduce the running time of our graph approach. Finally, a real-world resequencing setting is investigated
Iterative beam search for car sequencing
No full textThe 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
Analysis and design of sequencing rules for car sequencing
No full textThis 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
Analysis and design of sequencing rules for car sequencing
No full textThis 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
