Simulation of low-volume mixed model assembly lines: Modeling aspects and case study

Ziarnetzky, Timm; Mönch, Lars; Biele, Alexander

doi:10.1109/wsc.2014.7020055

Cited by 12 publications

(6 citation statements)

References 14 publications

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“…Low-volume assembly lines with a high amount of product variants are characterized by a large amount of manual processes, buffer space constrains, specialized resources and external suppliers [7]. As stated by Ziarnetzky, lowvolume serial production is usually characterized by a small number of typically large-sized objects with a big number of necessary processing steps.…”

Section: High Product MIX and Low-volume Production Manufacturing Sysmentioning

confidence: 99%

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Production logistics design and development support through simulation-based optimization (WIP)

Zúñiga

Moris

Syberfeldt

2016

2016 Summer Computer Simulation Conference (SCSC 2016)

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Manufacturing sectors in Sweden have a long history that leads to common non-optimized flows on the shop floor. Especially when having a really high product mix and a low-volume of customized products, a great deal of effort with respect to flow optimization is needed to stay present and compete in the globalized market. The goal of this project is to support the design and development of the implementation of new production systems and logistics flows considering the shop floor plant layout of a Swedish middle-size water pumps factory. In this paper, with the help of different types of simulation models and optimization, some results of a new technologically adapted production line are analyzed and relevant information and potential improvements in the production are found. The further development of optimization studies using the exiting simulation models is stated as ongoing and future work. The obtained and potential results can serve for decision makers and stakeholders to apply changes and adaptations in the system considering the mid and long term goals of the company.

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Section: High Product MIX and Low-volume Production Manufacturing Sysmentioning

confidence: 99%

“…In his paper an extensive literature review and description of the different logistics types [7] analyses an aircraft production case study using DES. This paper is more focus on the modelling aspects, different modelling blocks and the analysis of the inventory and buffer levels.…”

Section: Line Feeding Problemmentioning

confidence: 99%

Production logistics design and development support through simulation-based optimization (WIP)

Zúñiga

Moris

Syberfeldt

2016

2016 Summer Computer Simulation Conference (SCSC 2016)

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“…The method is commonly used to analyze complex processes that are challenging with closed-form analytical methods. DES is widely used for supply chain management analysis such as manufacturing process and logistics planning [38,47,66]. Simulations enable the design of the supply chain, and the evaluation of supply chain management prior to implementation of the system to perform what-if analysis [62].…”

Section: Introductionmentioning

confidence: 99%

Efficient propagation of uncertainties in manufacturing supply chains: Time buckets, L-leap, and multilevel Monte Carlo methods

Chiang¹,

Lin²,

Long³

2020

Operations Research Perspectives

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Uncertainty propagation of large scale discrete supply chains can be prohibitive when a large number of events occur during the simulated period and discrete event simulations (DES) are costly. We present a time bucket method to approximate and accelerate the DES of supply chains. Its stochastic version, which we call the L(logistic)-leap method, can be viewed as an extension of the leap methods, e.g., τ -leap [34], Dleap [6], developed in the chemical engineering community for the acceleration of stochastic DES of chemical reactions. The L-leap method instantaneously updates the system state vector at discrete time points and the production rates and policies of a supply chain are assumed to be stationary during each time bucket.We propose to use Multilevel Monte Carlo (MLMC) to efficiently propagate the uncertainties in a supply chain network, where the levels are naturally defined by the sizes of the time buckets of the simulations.We demonstrate the efficiency and accuracy of our methods using four numerical examples derived from a real world manufacturing material flow. In these examples, our multilevel L-leap approach can be faster than the standard Monte Carlo (MC) method by one or two orders of magnitudes without compromising the accuracy.

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“…First, they used a variable neighborhood search (VNS) approach to assign to each station a number of workers and processing times for each jobs. Then, they built a simulation model based on the simulation framework proposed by Ziarnetzky et al [Ziarnetzky et al, 2014] to evaluate the performance of the deterministic schedules under uncertainty. By means of the simulation, the trade off between stop and fix and travelling work strategies was analysed.…”

Section: Assembly Line Balancing Problemsmentioning

confidence: 99%

“…Heike published a case study to compare the use of constant or variable cycle times within a flow shop [Heike et al, 2001]. Ziarnetzky [Ziarnetzky et al, 2014] used discrete-event simulation for analysing different inventory policies during a station of a final assembly line. It considers supplier and production disruptions.…”

Section: Literature Reviewmentioning

confidence: 99%

Scheduling with limited resources along the aeronautical supply chain : from parts manufacturing plants to final assembly lines

Sanchidrián¹

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Por último, gracias a todos los amigos y familia que me han apoyado, preguntando con sincero interés por el avance de la tesis y posibilitándome horas de estudio. v ACKNOWLEDGEMENTS P art of this thesis was developed within HEFESTO project, partially funded by the European Regional Development Fund (ERDF) and the Spanish Ministerio de Economía y Competitividad, through its CDTI (Centro para el Desarrollo Tecnológico industrial) with project number ITC-20151126. vii

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Simulation of low-volume mixed model assembly lines: Modeling aspects and case study

Cited by 12 publications

References 14 publications

Production logistics design and development support through simulation-based optimization (WIP)

Production logistics design and development support through simulation-based optimization (WIP)

Efficient propagation of uncertainties in manufacturing supply chains: Time buckets, L-leap, and multilevel Monte Carlo methods

Scheduling with limited resources along the aeronautical supply chain : from parts manufacturing plants to final assembly lines

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