@article {438,
	title = {A leakage monitoring system of pipeline in heating network},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {462 - 469},
	abstract = {Supervisory control and data acquisition system (SCADA) is used to monitor the operating data of heating network. But it can do data acquisition and some simple operations only. So in this paper with the data collected by SCADA a leakage monitoring system of pipeline in heating network is designed by the Matble graphical user interface program (GUI). The theoretical basis of this monitoring is the heating network pressure graph theory and the theory of genetic algorithm. With the theory of genetic algorithm the data col-lected by SCADA could be optimized. The optimization avoid the erroneous data which due to measurement error. With the heating network graph theory the leakage monitoring system of pipeline could identify if there is a leakage in the operating conditions in heating pipe network. In the leakage monitoring system, the first step is to collect the initial data which optimized by genetic algorithm as a standard data and these data are stored as a fixed value. After that this system collects the real-time data of heating network and optimizes these data. Finally, the system compares the optimizing real-time data and the fixed value to determine whether there is a leakage. With the experiment the monitoring system which designed in this paper is proved to be very useful in leakage discrimination.},
	keywords = {Genetic Algorithm, Heating Network, MATLAB, Pressure Graph},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84892175513\&partnerID=40\&md5=1c08ed8b399411798f88e31b5dd14cec},
	author = {Xianliang, Y. and Lianlian, J. and Songling, W.}
}
@article {425,
	title = {The modeling of genetic and Tabu search algorithm based BP neural network in the risk analysis of investment},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Cited By (since 1996):2Export Date: 10 July 2014},
	month = {2013},
	pages = {391 - 399},
	abstract = {There are many parameters which affect the economic benefit indicators for evaluation and they are all uncertain. Because of the parameters{\textquoteright} uncertainty, evaluation indicators values of economic benefit in project investment are also uncertain. In these years, more and more BP Neural network based models are established to evaluate the risk of investment for its characters of wide information distribution and strong fault tolerance. However, the internal defect of local minima problem in this model will greatly discount the effects of evaluation if the training data are not proposed selected. To solve this problem of traditional BP neural network, an optimization algorithm that combines the advantages of genetic algorithm (GA) and Tabu Search (TS) is proposed. The training process is divided into two phases in order to search promising parameters of the BP neural network in favor of better evaluation and analysis of the investment. In the first phase, the initial parameters are first searched by GA algorithm by taking advantage of its various searches of the solutions, and the best parameters are selected by Tabu search algorithm in the second phases. After calculation of practical data, it proves that the new algorithm has better convergence rate and predictive accuracy, which make the evaluation and risk analysis of the investment faster and more accurate.},
	keywords = {BP Neural Network, Genetic Algorithm, Investment Evaluation, Risk Analysis, Tabu Search Algorithm},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84892147727\&partnerID=40\&md5=33572128b86e13bfcb8fb0b8e2c32994},
	author = {Yang, L. and Yunlong, D.}
}
@article {379,
	title = {Modeling resource-constrained project scheduling problem and its solution by genetic algorithm},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Cited By (since 1996):1Export Date: 10 July 2014},
	month = {2013},
	pages = {87 - 96},
	abstract = {The optimization of the resource-constrained project scheduling is an NP-hard problem. Complexity of the algorithm for solving this problem increases exponentially with the increase of resource constraints. Therefore, traditional optimization methods based on Excel tables cannot calculate the efficient allocation of resources, which will affect the scheduling of project management resources. The optimization problem like multiple resource-constrained project scheduling demands an optimization method based on genetic algorithm, which adopts the decimal encoding based on activity priority combined with the storage of the adjacency matrix in order to effectively solve unauthorized phenomenon in activity scheduling. It also adopts preemption mode of allocation of resources in activities to avoid conflict in resource allocation. Besides, we designs C language program that realizes heuristic genetic algorithm, which can find out the most excellent duration that meets the resource constraints through multiple iterations. Finally, the practical results indicate that genetic algorithm can quickly and efficiently solve this problem.},
	keywords = {Genetic Algorithm, NP-hard problem, Project scheduling, Resource constraints},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84879108012\&partnerID=40\&md5=6b5486fdec78b400439deebbf407f5b0},
	author = {Chai, C.}
}
@article {388,
	title = {Production line production planning model of iron and steel enterprise},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {131 - 135},
	abstract = {At present, iron and steel enterprise develops towards the direction with many procedure, many process, many variety and many specification, and reaches hundreds and thousands of product series and product mix, how to plan, organize and control steel production, production schedule of its product line are key issues. According to the main characteristics of production line of iron and steel enterprise, how to make aggregate production planning of iron and steel enterprise are analyzed. An aggregate production plan model of product line of iron and steel enterprise is proposed, which is calculated by using standard library function of the Matlab7.0 genetic algorithm toolbox to program. Penalty function was adopted in the course of getting solution. These parameters of scale of father population, crossover probability, mutation probability and penalty factor were combined and optimized. The result obtained is in accordance with the actual conditions of this enterprise basically that the optimization solution to production plans of real iron and steel enterprise by using the algorithm.},
	keywords = {Aggregate production plan model, Genetic Algorithm, Index terms -iron and steel enterprise, MATLAB, Production line},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84879110969\&partnerID=40\&md5=f4274b55fafe675f163de08cb3f49a87},
	author = {Jiang, G. and He, E. and Li, G. and Kong, J. and Xie, L.}
}
@article {380,
	title = {Vehicle routing optimization problem with time-windows and its solution by genetic algorithm},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {136 - 145},
	abstract = {Vehicle Routing Problem (VRP) is a hot issue in the field of logistics. Furthermore, the Vehicle Routing Problem with Time-Windows (VRPTW) is an additional constraint of the customer access time windows based on the VRP. It becomes a noticeable problem with the development of logistics distribution and the just-intime distribution demand from customers, which increases a visiting window to traditional VRP. This paper firstly analyzes the distribution vehicle scheduling optimization problem with time-windows. Subsequently, through transforming the capacity and soft time-windows constraint into objective constraint, the authors establish a non-full loaded VSP model. Moreover, genetic algorithm is used to solve this problem, where natural number is adopted to realize problem coding and the maximum reservation crossover and improved inverse mutation technology are used to generate new individuals. Finally through the simulation experiment, the results verify its feasibility and effectiveness.},
	keywords = {Genetic Algorithm, Logistics distribution, Time-windows, Vehicle routing optimization},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84879123858\&partnerID=40\&md5=7522f4b2d16a9e979a5ea8b102a48e5a},
	author = {Chen, T. and Zhou, G.}
}
@article {306,
	title = {A new range-free localization algorithm in wireless sensor networks based on improved genetic algorithm by control on adrenaline levels},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Cited By (since 1996):1Export Date: 10 July 2014},
	month = {2012},
	pages = {50 - 54},
	abstract = {In this paper, a new method is proposed to improve genetic algorithm (GA) based on control on adrenaline levels and a new range-free localization algorithm is designed based on the new method to increase the accuracy of the localization in wireless sensor networks (WSN). We choose the classic localization algorithm{\"y} Centroid Algorithm, to perform experiments to demonstrate the localization accuracy of the new algorithm. The result shows that the new algorithm we proposed provides better localization accuracy and it can optimize the localization algorithm in WSN.},
	keywords = {Control on Adrenaline Levels, Genetic Algorithm, Localization Accuracy, Wireless sensor networks},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84863283296\&partnerID=40\&md5=8d2ce76d7be6aef5a62301c217b62a78},
	author = {Liu, Z. and Fang, Z.}
}
