A hybrid dynamic pre-emptive and competitive neural-network approach in solving the multi-objective dispatching problem for TFT-LCD manufacturing

Yang, Taho; Lu, Jiunn Chenn

doi:10.1080/00207540902926514

Cited by 12 publications

(3 citation statements)

References 34 publications

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“…Learning methods optimize the updates of solutions, which thus improve the efficiency of optimization. Yang and Lu et al [20] proposed a hybrid dynamic preemptive and competitive NN approach called the advanced preventive competitive NN method. A CNN was used to classify the system conditions into 50 groups.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation

Zang

Wang

Song

et al. 2019

Computational Intelligence and Neuroscience

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In this paper, a hybrid deep neural network scheduler (HDNNS) is proposed to solve job-shop scheduling problems (JSSPs). In order to mine the state information of schedule processing, a job-shop scheduling problem is divided into several classification-based subproblems. And a deep learning framework is used for solving these subproblems. HDNNS applies the convolution two-dimensional transformation method (CTDT) to transform irregular scheduling information into regular features so that the convolution operation of deep learning can be introduced into dealing with JSSP. The simulation experiments designed for testing HDNNS are in the context of JSSPs with different scales of machines and jobs as well as different time distributions for processing procedures. The results show that the MAKESPAN index of HDNNS is 9% better than that of HNN and the index is also 4% better than that of ANN in ZLP dataset. With the same neural network structure, the training time of the HDNNS method is obviously shorter than that of the DEEPRM method. In addition, the scheduler has an excellent generalization performance, which can address large-scale scheduling problems with only small-scale training data.

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Section: Related Workmentioning

confidence: 99%

Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation

Zang

Wang

Song

et al. 2019

Computational Intelligence and Neuroscience

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“…Yang and Lu (2010) propose a hybrid dynamic preemptive and competitive NN approach called advanced preemptive competitive NN method. A preemptive method, which represents the multiple goals as a single objective function, is used for the multiobjective decision.…”

Section: Review Of Knowledge-based Approachesmentioning

confidence: 99%

Dynamic scheduling of manufacturing systems using machine learning: An updated review

Priore

Gómez

Pino

et al. 2014

AIEDAM

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A common way of dynamically scheduling jobs in a manufacturing system is by implementing dispatching rules. The issues with this method are that the performance of these rules depends on the state the system is in at each moment and also that no "ideal" single rule exists for all the possible states that the system may be in. Therefore, it would be interesting to use the most appropriate dispatching rule for each instance. To achieve this goal, a scheduling approach that uses machine learning can be used. Analyzing the previous performance of the system (training examples) by means of this technique, knowledge is obtained that can be used to decide which is the most appropriate dispatching rule at each moment in time. In this paper, a literature review of the main machine learning based scheduling approaches from the last decade is presented.

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“…Kohli and Gupta (2010) apply TOC on a small family owned pizza restaurant. Yang and Lu (2010) propose a hybrid dynamic pre-emptive and competitive neural-network approach to solve a two-workstation multi-objective dispatching decision problem from TFT-LCD manufacturing. Three production objectives are considered in this research as: cycle time, slack time and throughput.…”

Section: Introductionmentioning

confidence: 99%

Implementing lean standard work to solve a low work-in-process buffer problem in a highly automated manufacturing environment

Lu¹,

Yang

2014

International Journal of Production Research

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Over the past few decades, a considerable number of studies have been reported on assembly lines or less automated factories. Little attention has been given to implementing lean tools to a highly automated manufacturing environment. It is, therefore, necessary to make a more highly automated factory lean by considering both the manufacturing system variability and demand uncertainty. The purpose of this paper is to propose an effective lean tool to help practical lean participants successfully implement lean practices in a highly automated manufacturing environment. This study presents an example of how lean standard work is implemented and the throughput of a pacemaker workstation is improved by solving the low work-in-process buffer problem. A practical case from a photovoltaic module process with a semi-automated production line is used to illustrate the proposed method. The implementation results are promising. They showed a 37.5% labour reduction prior to the pacemaker workstation and a 304.7% increase in the daily throughput at the bottleneck workstation.

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A hybrid dynamic pre-emptive and competitive neural-network approach in solving the multi-objective dispatching problem for TFT-LCD manufacturing

Cited by 12 publications

References 34 publications

Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation

Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation

Dynamic scheduling of manufacturing systems using machine learning: An updated review

Implementing lean standard work to solve a low work-in-process buffer problem in a highly automated manufacturing environment

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