The Car Resequencing Problem with Pull-Off Tables

Boysen, Nils; Golle, Uli; Rothlauf, Franz

doi:10.1007/bf03342757

Cited by 20 publications

(8 citation statements)

References 25 publications

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“…Second, along with the aforesaid process and operation-based strategies, the product-oriented approaches listed in the upper left quadrant of Table 1 utilize the similarities and disparities of product characteristics. Several studies confirm the (Ding and Sun 2004;Lahmar and Benjaafar 2007;Gujjula and Günther 2009;Boysen et al 2011;Franz et al 2014Franz et al , 2015Moetz et al 2019) Polato et al 2018) influence of substitution approaches, such as the flexibilization of the sequence, by postponing the assignment of products to customer orders (Fournier and Agard 2007) or by decoupling orders from physical products (Inman and Schmeling 2003). The latter, usually referred to as virtual resequencing, is extensively analyzed (Inman and Schmeling 2003;Fournier and Agard 2007;Meißner et al 2008;Boysen et al 2009).…”

Section: Literature Reviewmentioning

confidence: 95%

“…Besides the dimensioning of buffers and acknowledging joint capacity restrictions, Urnauer et al ( 2019) provide a simulation-based optimization methodology for the location and allocation of buffer spaces between subsequent production steps. Present buffer operating strategies are based either on the utilization of downstream buffers (Boysen et al 2011) or on specific shuffling areas that change the order of products. The harmful impact of inserting rescheduled products into an existing sequence in terms of worker utilization and work distribution can be reduced by employing a non-trivial strategy (Lahmar and Benjaafar 2007;Gujjula and Günther 2009;Franz et al 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

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AI for in-line vehicle sequence controlling: development and evaluation of an adaptive machine learning artifact to predict sequence deviations in a mixed-model production line

Stauder

Kühl

2021

Flex Serv Manuf J

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Customers in the manufacturing sector, especially in the automotive industry, have a high demand for individualized products at price levels comparable to traditional mass-production. The contrary objectives of providing a variety of products and operating at minimum costs have introduced a high degree of production planning and control mechanisms based on a stable order sequence for mixed-model assembly lines. A major threat to this development is sequence scrambling, triggered by both operational and product-related root causes. Despite the introduction of Just-in-time and fixed production times, the problem of sequence scrambling remains partially unresolved in the automotive industry. Negative downstream effects range from disruptions in the Just-in-sequence supply chain, to a discontinuation of the production process. A precise prediction of sequence deviations at an early stage allows the introduction of counteractions to stabilize the sequence before disorder emerges. While procedural causes are widely addressed in research, the work at hand requires a different perspective involving a product-related view. Built on unique data from a real-world global automotive manufacturer, a supervised classification model is trained and evaluated. This includes all the necessary steps to design, implement, and assess an AI-artifact, as well as data gathering, preprocessing, algorithm selection, and evaluation. To ensure long-term prediction stability, we include a continuous learning module to counter data drifts. We show that up to 50% of the major deviations can be predicted in advance. However, we do not consider any process-related information, such as machine conditions and shift plans, but solely focus on the exploitation of product features like body type, power train, color, and special equipment.

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Section: Literature Reviewmentioning

confidence: 95%

Section: Literature Reviewmentioning

confidence: 99%

AI for in-line vehicle sequence controlling: development and evaluation of an adaptive machine learning artifact to predict sequence deviations in a mixed-model production line

Stauder

Kühl

2021

Flex Serv Manuf J

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“…The re-sequencing problem is formulated as the minimal violation of a set of rules for sequencing. Boysen et al [11] also discussed the resequencing problem of pull-off tables. They [12] also studied the problem of selectivity banks.…”

Section: Literature Reviewmentioning

confidence: 99%

Promoting Flexibility in the Mixed-model Assembly Line by Introducing Buffer Stocks

Yin

Matsuura

Asada

2014

iscm

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: The purpose of this paper is to clarify the increase in flexibility by introducing buffer stocks that enable modelsequence changes in a mixed-model assembly line. It is shown that changing the model sequences between stations can shorten the line length for a mixed-model assembly. Numerical analysis indicates that the range of possible operation time combinations of models can be increased and the model mix can be made more flexible by introducing buffer stocks under the same line length. This phenomenon is also discussed from the viewpoint of the relationship between buffers and flexibility. A procedure for examining feasible combinations of operation times is presented.

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“…Gusikhin et al (2008) find optimal FA entrance sequence that minimizes the violation from FA station capacities for ASRS type buffer. Boysen et al (2010Boysen et al ( , 2011 consider same problem for other common buffer type, pull-off table. They also perform a numerical study to determine buffer capacity.…”

Section: Introductionmentioning

confidence: 99%

A two-stage stochastic rule-based model to determine pre-assembly buffer content

Günay

Kula

2018

J Ind Eng Int

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This study considers instant decision-making needs of the automobile manufactures for resequencing vehicles before final assembly (FA). We propose a rule-based two-stage stochastic model to determine the number of spare vehicles that should be kept in the pre-assembly buffer to restore the altered sequence due to paint defects and upstream department constraints. First stage of the model decides the spare vehicle quantities, where the second stage model recovers the scrambled sequence respect to pre-defined rules. The problem is solved by sample average approximation (SAA) algorithm. We conduct a numerical study to compare the solutions of heuristic model with optimal ones and provide following insights: (i) as the mismatch between paint entrance and scheduled sequence decreases, the rule-based heuristic model recovers the scrambled sequence as good as the optimal resequencing model, (ii) the rule-based model is more sensitive to the mismatch between the paint entrance and scheduled sequences for recovering the scrambled sequence, (iii) as the defect rate increases, the difference in recovery effectiveness between rule-based heuristic and optimal solutions increases, (iv) as buffer capacity increases, the recovery effectiveness of the optimization model outperforms heuristic model, (v) as expected the rule-based model holds more inventory than the optimization model.

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The Car Resequencing Problem with Pull-Off Tables

Cited by 20 publications

References 25 publications

AI for in-line vehicle sequence controlling: development and evaluation of an adaptive machine learning artifact to predict sequence deviations in a mixed-model production line

AI for in-line vehicle sequence controlling: development and evaluation of an adaptive machine learning artifact to predict sequence deviations in a mixed-model production line

<b>Promoting Flexibility in the Mixed-model Assembly Line by Introducing </b><b>Bu</b>ff<b>er Stocks </b>

A two-stage stochastic rule-based model to determine pre-assembly buffer content

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