Data-driven Manufacturing Service Optimization Model in Smart Factory

2019 ◽  
Author(s):  
Wu Wei ◽  
Lu JianFeng ◽  
Zhang Hao
Keyword(s):  
Optimization Model ◽  
Data Driven ◽  
Smart Factory ◽  
Service Optimization

scholarly journals Hydraulic optimization of corrugated stilling basin with adverse slope

2018 ◽  
Vol 19 (1) ◽  
pp. 313-322 ◽  
Author(s):  
Tooraj Honar ◽  
Nafiseh Khoramshokooh ◽  
Mohammad Reza Nikoo
Keyword(s):  
Optimization Model ◽  
Hydraulic Jump ◽  
Experimental Results ◽  
Data Driven ◽  
Design Parameters ◽  
Algorithm Optimization ◽  
Stilling Basin ◽  
Design Variables ◽  
Bed Slope ◽  
Mlp Model

Abstract In this paper, perhaps for the first time, a data-driven simulation–optimization model is developed based on experimental results to find the effects of state and decision variables on the optimum characteristics of a stilling basin with adverse slope and corrugated bed. The optimal design parameters of the stilling basin are investigated to minimize the length of the hydraulic jump and ratio of the sequent depths of the jump while the relative amount of energy loss is maximized. In order to model the relationship between design variables of the bed, the experimental results are converted to a data-driven simulation model on the basis of a multilayer perceptron (MLP) neural network. Then, the validated MLP model is used in a genetic algorithm optimization model in order to determine the optimum characteristics of the bed under the hydraulic jump considering the interaction between the bed design variables and the hydraulic parameters of the flow. Results indicate that the optimum values of bed slope and the diameter of the corrugated roughness (2r) can be considered as −0.02 and 20 millimetres, respectively.

scholarly journals An Optimization Model for Construction Stage and Zone Plans of Rockfill Dams Based on the Enhanced Whale Optimization Algorithm

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 466 ◽  
Author(s):  
Jun Zhang ◽  
Denghua Zhong ◽  
Mengqi Zhao ◽  
Jia Yu ◽  
Fei Lv
Keyword(s):  
Optimization Algorithm ◽  
Optimization Model ◽  
Large Scale ◽  
Flow Shop ◽  
Whale Optimization Algorithm ◽  
Data Driven ◽  
Construction Stage ◽  
Construction Scheme ◽  
Rockfill Dam ◽  
Whale Optimization

Rockfill dams are among the most complex, significant, and costly infrastructure projects of great national importance. A key issue in their design is the construction stage and zone optimization. However, a detailed flow shop construction scheme that considers the opinions of decision makers cannot be obtained using the current rock-fill dam construction stage and zone optimization methods, and the robustness and efficiency of existing construction stage and zone optimization approaches are not sufficient. This research presents a construction stage and zone optimization model based on a data-driven analytical hierarchy process extended by D numbers (D-AHP) and an enhanced whale optimization algorithm (EWOA). The flow shop construction scheme is optimized by presenting an automatic flow shop construction scheme multi-criteria decision making (MCDM) method, which integrates the data-driven D-AHP with an improved construction simulation of a high rockfill dam (CSHRD). The EWOA, which uses Levy flight to improve the robustness and efficiency of the whale optimization algorithm (WOA), is adopted for optimization. This proposed model is implemented to optimize the construction stages and zones while obtaining a preferable flow shop construction scheme. The effectiveness and advantages of the model are proven by an example of a large-scale rockfill dam.

scholarly journals Big Data-Driven Based Real-Time Traffic Flow State Identification and Prediction

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Hua-pu Lu ◽  
Zhi-yuan Sun ◽  
Wen-cong Qu
Keyword(s):  
Big Data ◽  
Real Time ◽  
Traffic Flow ◽  
Optimization Model ◽  
Bilevel Optimization ◽  
Data Driven ◽  
Stage Model ◽  
Flow State ◽  
Real Time Traffic ◽  
State Identification

With the rapid development of urban informatization, the era of big data is coming. To satisfy the demand of traffic congestion early warning, this paper studies the method of real-time traffic flow state identification and prediction based on big data-driven theory. Traffic big data holds several characteristics, such as temporal correlation, spatial correlation, historical correlation, and multistate. Traffic flow state quantification, the basis of traffic flow state identification, is achieved by a SAGA-FCM (simulated annealing genetic algorithm based fuzzyc-means) based traffic clustering model. Considering simple calculation and predictive accuracy, a bilevel optimization model for regional traffic flow correlation analysis is established to predict traffic flow parameters based on temporal-spatial-historical correlation. A two-stage model for correction coefficients optimization is put forward to simplify the bilevel optimization model. The first stage model is built to calculate the number of temporal-spatial-historical correlation variables. The second stage model is present to calculate basic model formulation of regional traffic flow correlation. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods.

Data-Driven Analysis for RFID-Enabled Smart Factory: A Case Study

2020 ◽  
Vol 50 (1) ◽  
pp. 81-88 ◽  
Author(s):  
Jiqiang Feng ◽  
Feipeng Li ◽  
Chen Xu ◽  
Ray Y. Zhong
Keyword(s):  
Data Driven ◽  
Smart Factory
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