Automation and parameters optimization in production line: a case of study

Jimenez, Marco Antonio Barranco; Gutierrez, Susana V.; Lizárraga, Giovanni; Garza, Mauricio A.; González, David; Acevedo, J.; Osorio, Mario C.; Rodríguez, Roberto

doi:10.1007/s00170-012-4409-4

Cited by 9 publications

(3 citation statements)

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“…El-Tamimi et al (2012) used Awesim simulation software for modelling the Assembly line. Simulation and design of experiments (DOE) was also carried for experimental study of optimising metal cutting line in press work (Jimeneez et al, 2012). Pawel (2010) considered machine breakdown, part specifications and changes in customer requirement for building the simulation model.…”

Section: Literature Surveymentioning

confidence: 99%

Improving performance of lock assembly line using lean and simulation approach

Rane¹,

Sunnapwar

Chari

et al. 2017

IJBPM

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Assembly line optimisation is an important process. When the system is complex, mathematical modelling and design of experiments becomes difficult. Hence process simulation approach is adopted here. Contribution of this research is improvement of performance of assembly line by maximising the measurement of performances like cycle time, throughput, resource utilisation and also by reducing cost. Key features of this research are development of eight different models for different input variables without disturbing existing set up. This what-if analysis is done using Arena process simulation software. Effect of layout, resource distribution, combination of operations, time study, Maynard operation sequencing techniques (MOST) has been studied here. Combinational effect of all these parameters are difficult to analyse analytically, hence unique simulation approach is selected. MOST is useful to find value added and non-value added activities. Lean techniques like kaizen, total productive maintenance, line balancing, low cost automation are used for different scenarios.

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Section: Literature Surveymentioning

confidence: 99%

Improving performance of lock assembly line using lean and simulation approach

Rane¹,

Sunnapwar

Chari

et al. 2017

IJBPM

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“…The published literature mainly focuses on production line methods (i.e., parameter model (Jimenez et al, 2012;Colledani and Tolio, 2011), queueing model (Millhiser andBurnetas, 2013;Manitz, 2008), Markov model (Wu et al, 2016Kim and Morrison, 2015), and particularly on simulation model (Saidabad and Taghizadeh, 2015;Abdelsalam and Bao, 2006)) while widely disregarding the non-conforming product problems, such as products rework and departure, other than considering and solving them. This study is attributable to the almost all ignoring the Changjun Li, Jinbin Zhang and Bo Li, Journal of Advanced Mechanical Design, Systems, and Manufacturing, Vol.12, No.2 (2018) non-conforming product problems for performance analysis and optimization in academic research, as well as in practice, despite its increasing significance.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis and optimization of queueing network production systems considering non-conforming products rework and departure

Zhang

2018

JAMDSM

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During the past decades, the production performance analysis and optimization have played a significant role in queueing network system production planning and operation design for the manufacturing plants, but how to model the optimized production system performance and solve it is facing great challenges. Since the more and more wide use of queueing network, the non-conforming product scenarios such as rework and departure, cause mass random interference. These problems when modelled in the conventional analysis and optimization models are extremely difficult to solve as they are coupled and NP-hard problems. This paper considers a modified M/M/n/m-first come first service (M/M/n/m-FCFS) queueing network approximation model that is developed to maximize utilization and profit by analysing the queue time and queue length of the products under the constraint conditions. A hybrid meta-heuristics is applied to solve the approximation model, both the service rate and buffer allocation of the queueing network system can be determined. Finally, to assess the effectiveness of the proposed method, extensive numerical experimental results from the approximation model are compared to the ARENA simulation, and sensitivity and validation analysis are also conducted, which prove it accurate, believable, and effective.Keywords : Production planning, Operation design, Analysis and optimization, Queueing network, Non-conforming product, Hybrid meta-heuristics, Approximation model IntroductionMost manufacturing companies are facing the challenge of quickly implementing flexible and complex production systems (Walid and Ali, 2002). But the production systems often experience worse performance for the unavoidable variability exists in reality. This phenomenon appears widely in queueing network and multi-product mixed production system. Performance analysis and optimization problems in queueing systems have received considerable attention in recent years, due to their wide applicability in the performance evaluation and optimal design of manufacturing systems and computer networks (Song et al., 1998). Now, the long serial lines analysis and optimization have been well studied using decomposition (Shi and Gershwin, 2014;Damodaran and Hulett, 2012;Cauffriez and Willaeys, 2006) or aggregation (Veeger et al., 2011;Belmansour and Nourelfath, 2008) methods for a long time, and extensive results are obtained. Although many studies have focused on solving the line optimization problems, the studies in which a large number of approaches are currently applied to research the performance of queueing network systems considering the non-conforming products are still extremely limited.Research in the field of performance optimization tends to focus on issue models, such as cycle time (CT) optimization (Gilenson et al., 2015), throughput (TH) optimization (Law et al., 2008), work-in-process (WIP) optimization (Reyes et al., 2013;Qiu, 2005), and buffer allocation optimization (Shihabudin et al., 2015;Nahas, 2014). The published literature...

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“…With the increasing volume production, it is necessary to create strategies to achieve quality products at large scale in less time. For that reason, manufacturing companies have chosen computer vision automation as the solution of the problem established above [18].…”

Section: Introductionmentioning

confidence: 99%

Naïve Screw Nut Classifier Based on Hu's Moment Invariants and Minimum Distance

et al. 2018

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In this paper, an algorithm for classification of screw nuts by means of digital image processing is presented. This work is part of a project where a production line was built, and is focused on the quality assessment section. The algorithm presented classifies among good and poor quality screw nuts passing by a conveyor belt, by computing Hu's moment invariants of its picture. Those moment invariants are the input of a minimum distance classifier, obtaining very competitive results compared with some other classification algorithms of the WEKA plattform.

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Automation and parameters optimization in production line: a case of study

Cited by 9 publications

References 1 publication

Improving performance of lock assembly line using lean and simulation approach

Improving performance of lock assembly line using lean and simulation approach

Performance analysis and optimization of queueing network production systems considering non-conforming products rework and departure

Naïve Screw Nut Classifier Based on Hu's Moment Invariants and Minimum Distance

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