A Novel Job-Shop Scheduling Strategy Based on Particle Swarm Optimization and Neural Network

Zhang, Z.; Guan, Ziming; Zhang, J.; Xie, Xuemei

doi:10.2507/ijsimm18(4)co18

Cited by 27 publications

(14 citation statements)

References 20 publications

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“…Prediction not only provides information on future traffic conditions, but also helps evaluate the control decisions before they are implemented. In addition, considering the accuracy and timeliness of the prediction model, the time series model [8]- [10], Kalman filter model [11], support vector machine [12], non-parametric regression method [13]- [15] and long-term and short-term memory neural network [16], etc. have also been proposed for the prediction of short-term traffic flow in urban road networks.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Road Speed Forecasting Based on Hybrid RBF Neural Network With the Aid of Fuzzy System-Based Techniques in Urban Traffic Flow

Jia²,

Mei

et al. 2020

IEEE Access

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With the rapid economic development, urban areas are seeing more and more vehicles, leading to frequent urban traffic congestion. To solve this problem, the forecasting of traffic parameters is essential, in which, road operating speed (hereinafter referred to as ''road speed'') is a key parameter for forecasting road congestion. This paper proposes a hybrid radial basis function (RBF) neural network algorithm for forecasting road speed. First, it proposes a fuzzy RBF neural network structure by combining the fuzzy logic system with the RBF neural network. Then, it incorporates factors such as weather, holidays and road grades into the input layer. Considering the uncertainty and sensitivity of the initial centre of the traditional membership function layer, it uses fuzzy C-means clustering to determine the centre and other parameters of the membership function layer. Then using the gradient descent method, it trains the weights between the fuzzy inference layer and the output layer. Finally, this paper trains the proposed hybrid RBF neural network with the traffic road network data and weather data of a city, and uses the trained hybrid neural network to predict the road speed and the congestion status. The prediction results show that, compared with simplex prediction methods, such as BP neural network, time series method, and RBF neural network, the hybrid RBF neural network has a higher forecasting accuracy, with the mean absolute percentage error (MAPE) being reduced to 6.4%. Experimental results verify the accurate forecasting, enhanced learning feature and mapping capability of this method in short-term road speed forecasting, indicating that it can provide reliable predicted values to help solve urban congestion problems. INDEX TERMS Urban traffic flow, speed prediction, fuzzy C-means, fuzzy-RBF, neural network.

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Section: Introductionmentioning

confidence: 99%

Short-Term Road Speed Forecasting Based on Hybrid RBF Neural Network With the Aid of Fuzzy System-Based Techniques in Urban Traffic Flow

Jia²,

Mei

et al. 2020

IEEE Access

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“…BP neural network has been applied to multi-disciplinary prediction. For example, Qin et al [43], Zhang et al [44], Hu [45], respectively, used this method to simulate and predict behavioral recognition, job-shop scheduling problem, and optimization of intelligent logistics distribution center. In view of the extensiveness and reliability of the model in the field of prediction, this study will use this method to predict the logistics demand scale of Guangdong province.…”

Section: Bp Neural Network Modelmentioning

confidence: 99%

Regional logistics demand forecasting: a BP neural network approach

Huang

Xie

Zhao

et al. 2021

Complex Intell. Syst.

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With the rapid development of e-commerce, the backlog of distribution orders, insufficient logistics capacity and other issues are becoming more and more serious. It is very significant for e-commerce platforms and logistics enterprises to clarify the demand of logistics. To meet this need, a forecasting indicator system of Guangdong logistics demand was constructed from the perspective of e-commerce. The GM (1, 1) model and Back Propagation (BP) neural network model were used to simulate and forecast the logistics demand of Guangdong province from 2000 to 2019. The results show that the Guangdong logistics demand forecasting indicator system has good applicability. Compared with the GM (1, 1) model, the BP neural network model has smaller prediction error and more stable prediction results. Based on the results of the study, it is the recommendation of the authors that e-commerce platforms and logistics enterprises should pay attention to the prediction of regional logistics demand, choose scientific forecasting methods, and encourage the implementation of new distribution modes.

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“…Job-shop production control is a classic problem in the combinatory optimization of scheduling rules and production control [1][2][3]. As intelligent and precision manufacturing becomes the trend of industrial production, it is of practical significance to study the job-shop production control.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning and Rule Scheduling in a Job-Shop Production Control System

Zhao¹,

Zhang²

2021

Int. j. simul. model.

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As intelligent and precision manufacturing becomes the trend of industrial production, it is of practical significance to study the job-shop production control. However, the existing studies have not provided an evaluation mechanism to reasonably measure the control efficiencies of different plans. The desired control objectives are not easily achieved for job-shop production control problems with dynamic changes. Therefore, this paper probes into the dynamic job-shop production control problem based on deep reinforcement learning and rule scheduling. Firstly, a multi-objective optimization model was established for the production control system of dynamic job-shop. Then, deep reinforcement learning was introduced to job-shop production control system to transform the dynamic job-shop production control problem. After that, the authors proposed a dynamic job-shop production control method based on deep reinforcement learning, and explained the collaboration strategy for multiple subsystems. The proposed method was proved effective through experiments.

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A Novel Job-Shop Scheduling Strategy Based on Particle Swarm Optimization and Neural Network

Cited by 27 publications

References 20 publications

Short-Term Road Speed Forecasting Based on Hybrid RBF Neural Network With the Aid of Fuzzy System-Based Techniques in Urban Traffic Flow

Short-Term Road Speed Forecasting Based on Hybrid RBF Neural Network With the Aid of Fuzzy System-Based Techniques in Urban Traffic Flow

Regional logistics demand forecasting: a BP neural network approach

Application of Machine Learning and Rule Scheduling in a Job-Shop Production Control System

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