The Car Resequencing Problem with Pull-Off Tables

The 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

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

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 stop 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, powertrain, color, and special equipment

Benchmarch results of a genetic algorithm for non-permutation flowshops using constrained buffers

This paper presents the performance study of a Genetic Algorithm, for the special case of a mixed model non-permutation flowshop production line, where resequencing is permitted when stations have access to intermittent or centralized resequencing buffers. The access to the buffers is restricted by the number of available buffer places and the physical size of the products. Results from other authors are compared which presented results on permutation sequences [1] or which treat a problem similar to the one studied in this work [2], based on the benchmark data provided by Taillard [3]. Improvements that come with the introduction of constrained resequencing buffers are highlighted

Performance study of a genetic algorithm for sequencing in mixted model non-permutation flowshops using constrained buffers

This paper presents the performance study of a Genetic Algorithm applied to a mixed model non-permutation flowshop production line. Resequencing is permitted where stations have access to intermittent or centralized resequencing buffers. The access to the buffers is restricted by the number of available buffer places and the physical size of the products. Characteristics such as the difference between the intermittent and the centralized case, the number of buffer places and the distribution of the buffer places are analyzed. Improvements that come with the introduction of constrained resequencing buffers are highlighted

Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads.

As metagenomic studies move to increasing numbers of samples, communities like the human gut may benefit more from the assembly of abundant microbes in many samples, rather than the exhaustive assembly of fewer samples. We term this approach leaderboard metagenome sequencing. To explore protocol optimization for leaderboard metagenomics in real samples, we introduce a benchmark of library prep and sequencing using internal references generated by synthetic long-read technology, allowing us to evaluate high-throughput library preparation methods against gold-standard reference genomes derived from the samples themselves. We introduce a low-cost protocol for high-throughput library preparation and sequencing

Overview on: sequencing in mixed model flowshop production line with static and dynamic context

In the present work a literature overview was given on solution techniques considering basic as well as more advanced and consequently more complex arrangements of mixed model flowshops. We first analyzed the occurrence of setup time/cost; existing solution techniques are mainly focused on permutation sequences. Thereafter we discussed objectives resulting in the introduction of variety of methods allowing resequencing of jobs within the line. The possibility of resequencing within the line ranges from 1) offline or intermittent buffers, 2) parallel stations, namely flexible, hybrid or compound flowshops, 3) merging and splitting of parallel lines, 4) re-entrant flowshops, to 5) change job attributes without physically interchanging the position. In continuation the differences in the consideration of static and dynamic demand was studied. Also intermittent setups are possible, depending on the horizon and including the possibility of resequencing, four problem cases were highlighted: static, semi dynamic, nearly dynamic and dynamic case. Finally a general overview was given on existing solution methods, including exact and approximation methods. The approximation methods are furthermore divided in two cases, know as heuristics and methaheuristic

Genetic algorithm for sequencing in midex model non-permutation flowshops using constrained buffers

Este trabajo presenta un Algoritmo Genético (GA) del problema de secuenciar unidades en una línea de producción. Se tiene en cuenta la posibilidad de cambiar la secuencia de piezas mediante estaciones con acceso a un almacén intermedio o centralizado. El acceso al almacén además está restringido, debido al tamaño de las piezas. Abstract This paper presents a Genetic Algorithm (GA) for the problem of sequencing in a mixed model non-permutation flowshop. Resequencing is permitted where stations have access to intermittent or centralized resequencing buffers. The access to a buffer is restricted by the number of available buffer places and the physical size of the products

Analysis and adjustment of a genetic algorithm for non-permutation flowshops

The viability of many heuristic procedures strongly depends on the adequate adjustment of parameters. This work presents an adjustment procedure which was applied to a Genetic Algorithm. First, a preliminary analysis is performed, intended to obtain a better understanding of the behavior of the parameters, as for example to estimate how likely it is for the preceding adjustment of the parameters to remain in local minima. Special attention is paid on the variability of the solutions with respect to their repeatability. The four phases of the adjustment procedure are Rough-Adjustment, Repeatability, Clustering and Fine Adjustment

Manufacturing System and Supply Chain Analyses Related to Product Complexity and Sequenced Parts Delivery

Mixed model assembly has been widely used in many industries. It is applied in order to effectively deal with increasing product complexity. Sequencing and resequencing on a mixed-model assembly line is also complicated by high product complexity. To improve the performance of a mixed-model assembly system and the supply chain, one can develop efficient sequencing rules to address sequencing problems, and manage product complexity to reduce its negative impact on the production system. This research addresses aspects of sequence alteration and restoration on a mixed-model assembly line for the purpose of improving the performance of a manufacturing system and its supply chain, and addresses product complexity analysis. This dissertation is organized into Parts 1, 2, and 3 based on three submitted journal papers. Part 1. On a mixed-model assembly line, sequence alteration is generally used to intentionally change the sequence to the one desired by the downstream department; and sequence restoration is generally applied to achieve sequence compliance by restoring to the original sequence that has been unintentionally changed due to unexpected reasons such as rework. Rules and methods for sequence alteration using shuffling lines or sorting lines were developed to accommodate the sequence considerations of the downstream department. A spare units system based on queuing analysis was proposed to restore the unintentionally altered sequence in order to facilitate sequenced parts delivery. A queuing model for the repairs of defective units in the spare units system was developed to estimate the number of spare units needed in this system. Part 2. Research was conducted on product complexity analysis. Data envelopment analysis (DEA) was first applied to compare product complexity related to product variety among similar products in the same market, two DEA models including their respective illustrative models considering various product complexity factors and different comparison objectives were developed. One of these models compared the product complexity factors in conjunction with sales volume. The third DEA model was developed to identify product complexity reduction opportunities by ranking various product attributes. A further incremental economic analysis considering the changes in costs and market impact by an intended complexity change was presented in order to justify a product complexity reduction opportunity identified by the DEA model. Part 3. Two extended DEA models were developed to compare the relative complexity levels of similar products specifically in automobile manufacturing companies. Some automobile product attributes that have significant cost impact on manufacturing and the supply chain were considered as inputs in the two extended DEA models. An incremental cost estimation approach was developed to estimate the specific cost change in various categories of production activities associated with a product complexity change. A computational tool was developed to accomplish the cost estimation. In each of the above stated parts, a case study was included to demonstrate how these developed rules, models, or methods could be applied at an automobile assembly plant. These case studies showed that the methodologies developed in this research were useful for better managing mixed-model assembly and product complexity in an automobile manufacturing system and supply chain