Network Model and Optimization of Medical Waste Reverse Logistics by Improved Genetic Algorithm

2009 ◽  
pp. 40-52 ◽  
Author(s):  
Lihong Shi ◽  
Houming Fan ◽  
Pingquan Gao ◽  
Hanyu Zhang
Keyword(s):  
Genetic Algorithm ◽  
Network Model ◽  
Reverse Logistics ◽  
Medical Waste ◽  
Improved Genetic Algorithm

Location model for a remanufacturing reverse logistics network based on adaptive genetic algorithm

SIMULATION ◽  
2019 ◽  
Vol 95 (11) ◽  
pp. 1069-1084 ◽  
Author(s):  
Rui Yan ◽  
Bo Yan
Keyword(s):  
Genetic Algorithm ◽  
Network Model ◽  
Reverse Logistics ◽  
Closed Loop ◽  
Value Added ◽  
Open Loop ◽  
Demand Elasticity ◽  
Mixed Integer ◽  
Adaptive Genetic Algorithm ◽  
Loop Model

Energy saving and environmental protection are important issues of today. Concerning the environmental and social need to increase the utilization of used products, this paper introduces two remanufacturing reverse logistics (RL) network models, namely, the open-loop model and the closed-loop model. In an open-loop RL system, used products are recovered by outside firms, while in a closed-loop RL system, they are returned to their original producers. The open-loop model features a location selection with two layers. For this model, a mixed-integer linear program (MILP) is built to minimize the total costs of the open-loop RL system, including the fixed cost, the freight between nodes, the operation cost of storage and remanufacturing centers, the penalty cost of unmet or remaining demand quantity, and the government-provided subsidy given to the enterprises that protect the environment. This MILP is solved using an adaptive genetic algorithm with MATLAB simulation. For a closed-loop RL network model, a special demand function considering the relationship between new and remanufactured products is developed. Remanufacturing rate, environmental awareness, service demand elasticity, value-added services, and their impacts on total profit of the closed-loop supply chain are analyzed. The closed-loop RL network model is proved effective through the analysis of a numerical example.

scholarly journals An Improved Genetic Algorithm Coupling a Back-Propagation Neural Network Model (IGA-BPNN) for Water-Level Predictions

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1795 ◽  
Author(s):  
Nengcheng Chen ◽  
Chang Xiong ◽  
Wenying Du ◽  
Chao Wang ◽  
Xin Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Water Level ◽  
Network Model ◽  
Neural Network Model ◽  
Local Convergence ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Improved Genetic Algorithm ◽  
Genetic Strategies

Accurate water-level prediction is of great significance to flood disaster monitoring. A genetic algorithm coupling a back-propagation neural network (GA-BPNN) has been adopted as a hybrid model to improve forecast performance. However, a traditional genetic algorithm can easily to fall into locally limited optimization and local convergence when facing a complex neural network. To deal with this problem, a novel method called an improved genetic algorithm (IGA) coupling a back-propagation neural network model (IGA-BPNN) is proposed with a variety of genetic strategies. The strategies are to supply a genetic population by a chaotic sequence, multi-type genetic strategies, adaptive dynamic probability adjustment and an attenuated genetic strategy. An experiment was tested to predict the water level in the middle and lower reaches of the Han River, China, with meteorological and hydrological data from 2010 to 2017. In the experiment, the IGA-BPNN, traditional GA-BPNN and an artificial neural network (ANN) were evaluated and compared using the root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE) coefficient and Pearson correlation coefficient (R) as the key indicators. The results showed that IGA-BPNN moderately correlates with the observed water level, outperforming the other two models on three indicators. The IGA-BPNN model can settle problems including the limited optimization effect and local convergence; it also improves the prediction accuracy and the model stability regardless of the scenario, i.e., sudden floods or a period of less rainfall.

scholarly journals Network Intrusion Detection Based on Sparse Autoencoder and IGA-BP Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hongli Deng ◽  
Tao Yang
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Network Model ◽  
Detection Rate ◽  
Network Intrusion Detection ◽  
Improved Genetic Algorithm ◽  
Bp Network ◽  
Model Based ◽  
Network Intrusion ◽  
Different Types

Network intrusion detection system provides a better network security solution than other traditional network defense technologies. Aiming at the increasingly serious problem of Internet security in the big data environment, a network intrusion detection model based on autoencoder network model and improved genetic algorithm BP (IGA-BP) network is constructed. In order to reduce the data dimension and eliminate redundant information, the autoencoder network model is firstly used to denoise and dedimension. A new population was formed by selecting some of the best parent individuals for cross mutation and replacing the worst parent individuals. The improved genetic algorithm and new population generation model will provide more reasonable initial parameters for BP network, namely, IGA-BP network model. Based on IGA-BP network model, the problems of slow detection rate and easy to get into local optimality in BP network are solved. The experiments were performed on KDD CUP99 dataset, which simulated different types of user organizations and different types of network intrusion. Compared with the existing intrusion detection methods, the experimental results show that the proposed method has a great effect on classification accuracy, false positives, and detection rate.

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