Using simulation to select part-families for cell manufacturing

Reeb, J. E.; Baker, Eric S.; Brunner, Charles C.; Funck, James W.; Reiter, William F.

doi:10.1179/002032010x12858356612744

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…Simulation is defined as mimicking the performance of a real system process and performing experiments with this model in order to understand system behavior or evaluate different strategies for system operation [123]. Reeb et al, used discrete event simulation to develop and select a family of components for cell production in a wood production company [124]. Durmusoglu and Satoglu, designed methods based on obvious principles for the cell production system.…”

Section: Simulation Methodsmentioning

confidence: 99%

Cell Production System Design: A Literature Review

Ghahremani-Nahr

Bathaee

Mazloumzadeh

et al. 2021

IJIMES

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In a cell production system, a number of machines that differ in function are housed in the same cell. The task of these cells is to complete operations on similar parts that are in the same group. Determining the family of machine parts and cells is one of the major design problems of production cells. Cell production system design methods include clustering, graph theory, artificial intelligence, meta-heuristic, simulation, mathematical programming. This article discusses the operation of methods and research in the field of cell production system design. Methodology:To examine these methods, from 187 articles published in this field by authoritative scientific sources, based on the year of publication and the number of restrictions considered and close to reality, which are searched using the keywords of these restrictions and among them articles Various aspects of production and design problems, such as considering machine costs and cell size and process routing, have been selected simultaneously.Findings: Finally, the distribution diagram of the use of these methods and the limitations considered by their researchers, shows the use and efficiency of each of these methods. By examining them, more efficient and efficient design fields of this type of production system can be identified.Originality/Value: In this article, the literature on cell production system from 1972 to 2021 has been reviewed.

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Section: Simulation Methodsmentioning

confidence: 99%

Cell Production System Design: A Literature Review

Ghahremani-Nahr

Bathaee

Mazloumzadeh

et al. 2021

IJIMES

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“…For example, if one machine is processing parts while the rest are just idle (i.e., one machine out of three is utilized), then the overall machine utilization is 33.3%; if this machine is processing just half of the time while the others are idle (i.e., one machine out of three is utilized only for half of its available time), then the overall machine utilization is 16.6%. Therefore, the overall machine utilization in this numerical example is 73.1% according to (8), and the production rate is 4.38 parts/min according to (9).…”

Section: B Multimachine Fmc Examplementioning

confidence: 99%

“…In those efforts, they have also modeled the performance of FMCs using different approaches, such as artificial intelligence, simulation, fuzzy logic, and so on. Reeb et al [8], for example, discussed the use of discrete event simulation to develop and select part families for cell manufacturing. Lee and Kim [9], Sawik [10], and Gultekin et al [11] are the examples of researchers who have designed mathematical algorithms for FMC scheduling.…”

Section: Introductionmentioning

confidence: 99%

Multimachine Flexible Manufacturing Cell Analysis Using a Markov Chain-Based Approach

Hamasha

Alazzam²,

Hamasha

et al. 2015

IEEE Trans. Compon., Packag. Manufact. Technol.

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In this paper, a stochastic model is developed to analyze the performance of a flexible manufacturing cell (FMC). The FMC considered in this paper consists of a single conveyor, a single robot, and one or more machine(s). The conveyor belt delivers the working part to the robot, which loads it onto the machine. A Markov chain model is constructed for one-machine and twomachine FMCs, after which the model is generalized to an FMC with n machines. Most importantly, the model provides an estimate of the overall machine utilization and production rate for the FMC under consideration and also illustrates the effect of different operational factors on machine utilization and production rate. The results indicated that the overall machine utilization increases with conveyor belt and robot delivery rates and decreases with machine rate, as expected. However, this decrease or the increase in the overall machine utilization is sharp at low levels of each parameter (e.g., conveyor belt delivery and robot loading), but it gradually stabilizes at higher levels of the parameters. Finally, the production rate increases sharply at low levels of each parameter and gradually stabilizes at higher levels.Index Terms-Flexible manufacturing cell (FMC), Markov chain, multiple machine, single machine.

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“…employed multivariate methods to determine optimal number of operators in a CMS by using results of simulation models. Reeb et al (2010) designed a CMS and then excluded some part families from job-shop floor in a wood manufacturing facility with simulation by using leadtime and work-in-process inventory. developed simulation models in order to optimise worker assignment in CMSs considering different layouts.…”

Section: Cell Formation and Simulationmentioning

confidence: 99%

Hybrid cellular manufacturing system design with cellularisation ratio: an integrated mixed integer nonlinear programming and discrete event simulation approach

Eğilmez

Erenay

Süer

2019

IJSOM

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This study investigates hybrid cellular manufacturing system design where products with highly similar process routes are formed as manufacturing cells, and products with dissimilar routes are produced in a process layout. A cell similarity threshold, cellularisation ratio (C-ratio), is proposed for product assignment to cells and process layout, and integrated into a mathematical modelling-based cell formation approach. The cell formation phase is carried out with the proposed mathematical model. Several case problems are tested, and selected cases are also studied with a newly developed simulation model where manufacturing system performance is also evaluated along with the proposed hybrid cellular manufacturing design alternatives. Results indicated that considering a C-ratio during cell formation can provide higher cell similarity thus better cell formation. The increase in the coverage levels resulted in higher number of cells formed. The proposed approach can be significantly beneficial for manufacturing design decisions where product mix and process complexity are high, and for a hybrid layout that consists of cellular plus process layouts could be implemented.

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Using simulation to select part-families for cell manufacturing

Cited by 5 publications

References 15 publications

Cell Production System Design: A Literature Review

Cell Production System Design: A Literature Review

Multimachine Flexible Manufacturing Cell Analysis Using a Markov Chain-Based Approach

Hybrid cellular manufacturing system design with cellularisation ratio: an integrated mixed integer nonlinear programming and discrete event simulation approach

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