01974nas a2200205   4500008004100000245006300041210006100104260000900165300001400174490000700188520133200195653002201527653002001549653001101569653001901580100001801599700001701617700001701634856011701651       2013                        eng d00aA leakage monitoring system of pipeline in heating network0 aleakage monitoring system of pipeline in heating network  c2013  a462 - 4690 v113 aSupervisory 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.10aGenetic Algorithm10aHeating Network10aMATLAB10aPressure Graph1 aXianliang, Y.1 aLianlian, J.1 aSongling, W.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84892175513&partnerID=40&md5=1c08ed8b399411798f88e31b5dd14cec02104nas a2200205   4500008004100000245011300041210006900154260000900223300001400232490000700246520138500253653002201638653002201660653002601682653001801708653002601726100001301752700001601765856011701781       2013                        eng d00aThe modeling of genetic and Tabu search algorithm based BP neural network in the risk analysis of investment0 amodeling of genetic and Tabu search algorithm based BP neural ne  c2013  a391 - 3990 v113 aThere are many parameters which affect the economic benefit indicators for evaluation and they are all uncertain. Because of the parameters' 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.10aBP Neural Network10aGenetic Algorithm10aInvestment Evaluation10aRisk Analysis10aTabu Search Algorithm1 aYang, L.1 aYunlong, D.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84892147727&partnerID=40&md5=33572128b86e13bfcb8fb0b8e2c3299401784nas a2200181   4500008004100000245009900041210006900140260000900209300001200218490000700230520114500237653002201382653002001404653002301424653002501447100001301472856011701485       2013                        eng d00aModeling resource-constrained project scheduling problem and its solution by genetic algorithm0 aModeling resourceconstrained project scheduling problem and its   c2013  a87 - 960 v113 aThe 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.10aGenetic Algorithm10aNP-hard problem10aProject scheduling10aResource constraints1 aChai, C.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84879108012&partnerID=40&md5=6b5486fdec78b400439deebbf407f5b001871nas a2200241   4500008004100000245007500041210006900116260000900185300001400194490000700208520110400215653003601319653002201355653004301377653001101420653002001431100001401451700001101465700001101476700001301487700001201500856011701512       2013                        eng d00aProduction line production planning model of iron and steel enterprise0 aProduction line production planning model of iron and steel ente  c2013  a131 - 1350 v113 aAt 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.10aAggregate production plan model10aGenetic Algorithm10aIndex terms -iron and steel enterprise10aMATLAB10aProduction line1 aJiang, G.1 aHe, E.1 aLi, G.1 aKong, J.1 aXie, L.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84879110969&partnerID=40&md5=f4274b55fafe675f163de08cb3f49a8701681nas a2200193   4500008004100000245009700041210006900138260000900207300001400216490000700230520100800237653002201245653002701267653001701294653003301311100001301344700001301357856011701370       2013                        eng d00aVehicle routing optimization problem with time-windows and its solution by genetic algorithm0 aVehicle routing optimization problem with timewindows and its so  c2013  a136 - 1450 v113 aVehicle 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.10aGenetic Algorithm10aLogistics distribution10aTime-windows10aVehicle routing optimization1 aChen, T.1 aZhou, G.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84879123858&partnerID=40&md5=7522f4b2d16a9e979a5ea8b102a48e5a01298nas a2200193   4500008004100000245014000041210006900181260000900250300001200259490000700271520057400278653003300852653002200885653002600907653002900933100001200962700001300974856011700987       2012                        eng d00aA new range-free localization algorithm in wireless sensor networks based on improved genetic algorithm by control on adrenaline levels0 anew rangefree localization algorithm in wireless sensor networks  c2012  a50 - 540 v103 aIn 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 algorithmÿ 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.10aControl on Adrenaline Levels10aGenetic Algorithm10aLocalization Accuracy10aWireless sensor networks1 aLiu, Z.1 aFang, Z.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84863283296&partnerID=40&md5=8d2ce76d7be6aef5a62301c217b62a78