Parameter Identification for Bernoulli Serial Production Line Model

Sun, Yuting; Zhu, Tianyu; Liang, Zhonghua; Denno, Peter

doi:10.1109/tase.2020.3035291

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…In existing studies, there are two ways to build computer simulation model of manufacturing systems: using a general purposed programming language such as C/C++/Python/MATLAB (e.g., References [21][22][23]) or using a DES software package such as Simul8, Arena, FlexSim, SimEvents, WITNESS, and so forth. With a graphical user interface (GUI), the simulation models built using the latter approach are usually more accessible, easier to manage, and more convenient to operate by common users.…”

Section: Simulation Toolsmentioning

confidence: 99%

A simulation‐based integrated virtual testbed for dynamic optimization in smart manufacturing systems

Sun

Bragin

et al. 2022

J Adv Manuf & Process

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In a manufacturing system, production control-related decision-making activities occur at different levels. At the process level, one of the main control activities is to tune the parameters of individual manufacturing equipment. At the system level, the main activity is to coordinate production resources and to route parts to appropriate workstations based on their processing requirement, priority indices, and control policy. At the factory level, the goal is to plan and schedule the processing of parts at different operations for the entire system in order to optimize certain objectives. Note that the results of such activities at different levels are closely coupled and affect the overall performance of the manufacturing system as a whole. Therefore, it is important to systematically integrate these control and optimization activities into one unified platform to ensure the goal of each individual activity is aligned with the overall performance of the system. In this paper, we develop a simulation-based virtual testbed that implements dynamic optimization, automatic information exchange, and decision-making from the process-level, system-level, and factory-level of a manufacturing system into an integrated computation environment. This is demonstrated by connecting a Python-based numerical computation program, discrete-event simulation software (Simul8), and an optimization solver (CPLEX) via a third-party master program. The application of this simulation-based virtual testbed is illustrated by a case study in a machining shop.

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Section: Simulation Toolsmentioning

confidence: 99%

A simulation‐based integrated virtual testbed for dynamic optimization in smart manufacturing systems

Sun

Bragin

et al. 2022

J Adv Manuf & Process

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, the design, implementation, and parameter identification and optimization of SPL have been reported in a number of research studies such as [34][35][36][37][38]. Among others, Sun et al [36] studied production lines characterized by the Bernoulli serial line model and developed algorithms to identify model parameters to fit the system throughput. Furthermore, Yan et al [38] proposed an improved aggregation method to improve the prediction accuracy of traditional aggregation method for the Bernoulli serial production lines with unreliable machines and finite buffers.…”

Section: A Throughput Evaluation Of Splmentioning

confidence: 99%

Development of an Efficient Prediction Model for Optimal Design of Serial Production Lines

et al. 2021

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One of the problems encountered in the design and implementation of a serial production line (SPL) is the buffer size between the machine tools. The buffer size of the SPL has an important impact on the productivity of the whole production system. The machine tools' characteristics including their uptimes and downtimes and the process parameters are the main factors that affect the decision regarding the buffer size, and thus the productivity of the SPL. Due to the dynamic nature of this problem, it is complex to find the optimal buffer size in SPL. Thus, in this paper, an Efficient Prediction Model (EPM) is developed using Artificial Neural Network (ANN). The purpose of the developed EPM is to find the buffer size between each succeeding pair of machine tools in SPL at any given uptimes and downtimes of machine tools. An optimization model based on genetic algorithms (GA) is used to generate the learning data for the prediction model to find the optimal or near optimal buffer size of the bay of each machine tool in SPL. The proposed approach integrates the optimization and prediction methodologies to evaluate, and predict the optimal buffer sizes for maximum productivity. Including uptime and downtime parameters enable the proposed method to be used to improve the design of running SPL as well as to design a new SPL. Numerical examples for five and fifteen machine tools were conducted independently in this research and the results show the ability of the proposed method to determine the optimal buffer sizes in a reasonable amount of time. In particular, the results of case studies show that the developed model accurately predict the optimal buffer size, especially for the case of five machines and even for a higher number of machine tools yet with acceptable but less accuracy. Finally, the performance of the proposed approach was compared with some results of the state of the art methods reported in the literature. The comparison shows the superiority of the present approach to identify buffer sizes for higher throughput under the same uptimes and downtimes.

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“…Representative results of analytical methods can be found in the paper by Wang, Huang, and Li [ 29 ], which investigated transient throughput properties of flexible systems with the finite buffer and Bernoulli machine. Further research includes a geometric serial line [ 30 ], assembly systems [ 31 ], a serial Bernoulli production line [ 32 , 33 , 34 ], and Bernoulli production systems with rework processes [ 35 , 36 ]. In contrast to computer simulation, analytical methods can inherently overcome the above drawbacks and disclose fundamentally mathematical coupling between performance measures and system parameters.…”

Section: Introductionmentioning

confidence: 99%

A Novel Analytical Modeling Approach for Quality Propagation of Transient Analysis of Serial Production Systems

Liu

et al. 2022

Sensors

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Production system modeling (PSM) for quality propagation involves mapping the principles between components and systems. While most existing studies focus on the steady-state analysis, the transient quality analysis remains largely unexplored. It is of significance to fully understand quality propagation, especially during transients, to shorten product changeover time, decrease quality loss, and improve quality. In this paper, a novel analytical PSM approach is established based on the Markov model, to explore product quality propagation for transient analysis of serial multi-stage production systems. The cascade property for quality propagation among correlated sequential stages was investigated, taking into account both the status of the current stage and the quality of the outputs from upstream stages. Closed-form formulae to evaluate transient quality performances of multi-stage systems were formulated, including the dynamics of system quality, settling time, and quality loss. An iterative procedure utilizing the aggregation technique is presented to approximate transient quality performance with computational efficiency and high accuracy. Moreover, system theoretic properties of quality measures were analyzed and the quality bottleneck identification method was investigated. In the case study, the modeling error was 0.36% and the calculation could clearly track system dynamics; quality bottleneck was identified to decrease the quality loss and facilitate continuous improvement. The experimental results illustrate the applicability of the proposed PSM approach.

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Parameter Identification for Bernoulli Serial Production Line Model

Cited by 8 publications

References 17 publications

A simulation‐based integrated virtual testbed for dynamic optimization in smart manufacturing systems

A simulation‐based integrated virtual testbed for dynamic optimization in smart manufacturing systems

Development of an Efficient Prediction Model for Optimal Design of Serial Production Lines

A Novel Analytical Modeling Approach for Quality Propagation of Transient Analysis of Serial Production Systems

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