多矿整合产能配置与区域开拓运输系统重构优化研究
|
摘要
矿山建设工程设计与规划是一项集技术、经济、资源及环境为一体,综合性较强的重大工程,而以多矿山整合而成的区域整体矿山在产能配置及开拓运输系统等问题上提出了更高的要求。本文在云锡公司实施资产重组与资源整合,构建以“三大平台”为基础的区域矿山建设背景下,开展多矿整合产能配置与区域开拓运输系统重构优化等研究。研究着眼于资源开发模式的创新和促进企业的跨越式发展,在和谐、统一、高效的理念下,运用现代非线性科学理论和方法、计算机模拟等技术,系统深入的分析和探讨了区域矿山采选匹配及优化、开拓运输系统重构、运输系统虚拟仿真及水资源安全预测与优化配置等问题。具体研究内容如下:
(1)应用遗传算法对选厂生产能力进行优化,建立多目标选厂生产能力遗传优化数学模型。根据遗传算法原理,结合区域矿山选厂生产能力优化的实质,即多目标、多因素同时具有连续和离散变量的组合优化,以矿山选矿能力总和为适应度,以适应度最大建立目标函数。通过优化设计变量选择,以供矿量、开拓运输系统工程布置和尾矿库库容为主要约束条件的引入,采用最佳化搜寻遗传算法软件实现遗传优化数学模型的求解。优化结果为选矿布局的调整,选矿能力结构的改善,及实现资源利用率和效益最大化提供决策依据。
(2)基于蚁群算法理论,构建区域地下矿山开拓系统的规划与优化模型。把复杂的区域矿山开拓系统优化问题转化为网络流问题,综合运用最小费用最大流,绝对中心等网络流理论对区域矿山开拓系统开展优化研究。提出基于蚁群算法的区域地下矿山开拓系统的规划模型,实现改进蚁群算法的计算机模拟,进行区域地下矿山开拓系统的蚁群算法优化。
(3)分析地下矿山开拓运输系统的特性,阐述计算机模拟的基本理论,建立矿山地下运输系统模拟模型。以离散事件系统模拟的建模方法为基础,结合时间序列随机模拟递推模型进行开拓运输系统模拟模型的总体设计,初步建立各运输子系统微观模型和区域矿山运输系统总模型。模拟模型包含功能模块和逻辑结构,并实现了运输网络特性、列车运动特征、运输信号控制、运输事故响应等要素的整合。
(4)以大型系统仿真软件Flexsim为研究平台,建立矿山运输系统仿真模型,实现区域矿山地下运输系统仿真。根据离散事件仿真理论,结合矿山地下运输系统模拟模型,以单轨、双轨运输模型单元构建各矿山子运输系统模型。进行子运输系统模型的合并得到区域运输系统的总体模型。根据模型研究在不同条件下运输系统的运行状况,评价运输系统运行效能,实现区域地下矿山运输系统决策的智能化和可视化。
(5)分析水资源系统混沌特征,在水资源安全领域中引入混沌理论,将混沌理论和神经网络相结合,建立相空间重构神经网络耦合的神经网络需水量预测模型。通过模型调试,参数误差与灵敏度分析,进行水资源预测模拟。通过对模拟值和历史统计数据的比较,以验证模型的有效性,反映个旧东区水资源承载力的特征及水资源系统状态的演化规律。
Designing and planning of mine construction is a strong integrity engineering include technology, economy, resource and environment, however, regional mine that make up of multi-mines put forward higher request on productivity configuration together with its development haulage system. On the basis of assets realignment, resources integration and construction of "Three Platform" of regional mine in Yunnan Tin Company, this paper focus on the study on multi-mines productivity configuration and regional development haulage system reconstruction and optimization. The research with a view to innovation of exploitation mode of resources and acceleration of great-leap-forward development of enterprise under the eidos of harmonious, unification and efficiency, which deeply analyze and discuss the problems such as optimization of selection and collection, optimization of development haulage system, virtual simulation of haulage system and safety prediction and optimized disposition of water resource by using modern non-linear scientific method and computer technology. The detailed research contents as follows:
(1) By using genetic algorithm (GA) optimizing the throughput of the concentration plant, and establishing a multi-objective optimization mathematic model of the concentrating plant. According to genetic algorithm theory, in combination with the essential of regional mine concentrating plant's throughput, which is multi-objective, multi-factor with continuous and discrete variables combinatorial optimization at the same time, taking the mine total ability of mineral processing as fitness, and establishing the objective function with the largest fitness. By optimizing design the choice of variables, developing transport systems engineering layout and the tailings storage capacity for the introduction of the main constraints, and use the best of the genetic algorithm search software to realize the solving of optimization mathematic model. The results of optimization supply the decision-making basis for adjusting the layout of mineral processing, improving the mineral processing ability structure of concentration plant, and realizing the maximum of resource utilization ratio and benefit.
(2) Constructing the planning and optimizing model of regional underground mine development system based on ant colony algorithm theory. Transforming the complicated regional mine development system optimization into a network flow, comprehensive application of the minimum cost maximum flow, the absolute center of the network flow theory to research the development system optimization of regional mine. Advancing the proposed regional system of underground mine development planning model based on the ant colony algorithm, achieving improve the computer simulation of ant colony algorithm, and the ant colony algorithm of regional underground mine development system.
(3) The paper analyzed the characteristic of underground mine development and transportation system, and expatiated the basic theory of computer simulation, and established the simulation model of underground mine transport system. Based on the discrete event system simulation, combining with stochastic time-series simulation recursive model to carry on the development and transport system simulation design, and the paper initially established each transport subsystem micro-model and the model of whole regional mine transport system. The simulation model includes functional modules and logic structure, achieving the integration of the character of transport network and train movement, transport signal control, transport accident response.
(4) The simulating model of transport system is established by using the large simulation software Flexsim to simulate regional mine underground transport system. According to the simulation theory of discrete events and mine underground transport system simulation model, setting up mine sub-transport system model with monorail and double transport model cell. Collectivity model of regional transport system is obtained by combining sub-transport system models. Studying on the operations of transport system at different situations in order to evaluate the operation effect and realize intelligent and visualization of decision-making.
(5) Analyzing the chaotic features of water resources system, by introducing the chaos theory into the field of water resources security combining with neural network to establish phase space reconstruction neural network coupled neural network model to predict water. By debugging model, analyzing parameter error and sensitivity to take on water resources forecasting simulation. Compare with the simulated values and historical of statistical data, verifying the validity of the model and reflecting the characteristics of water resources bearing capacity in the eastern of Gejiu and the state of the system evolution rule.
(1)应用遗传算法对选厂生产能力进行优化,建立多目标选厂生产能力遗传优化数学模型。根据遗传算法原理,结合区域矿山选厂生产能力优化的实质,即多目标、多因素同时具有连续和离散变量的组合优化,以矿山选矿能力总和为适应度,以适应度最大建立目标函数。通过优化设计变量选择,以供矿量、开拓运输系统工程布置和尾矿库库容为主要约束条件的引入,采用最佳化搜寻遗传算法软件实现遗传优化数学模型的求解。优化结果为选矿布局的调整,选矿能力结构的改善,及实现资源利用率和效益最大化提供决策依据。
(2)基于蚁群算法理论,构建区域地下矿山开拓系统的规划与优化模型。把复杂的区域矿山开拓系统优化问题转化为网络流问题,综合运用最小费用最大流,绝对中心等网络流理论对区域矿山开拓系统开展优化研究。提出基于蚁群算法的区域地下矿山开拓系统的规划模型,实现改进蚁群算法的计算机模拟,进行区域地下矿山开拓系统的蚁群算法优化。
(3)分析地下矿山开拓运输系统的特性,阐述计算机模拟的基本理论,建立矿山地下运输系统模拟模型。以离散事件系统模拟的建模方法为基础,结合时间序列随机模拟递推模型进行开拓运输系统模拟模型的总体设计,初步建立各运输子系统微观模型和区域矿山运输系统总模型。模拟模型包含功能模块和逻辑结构,并实现了运输网络特性、列车运动特征、运输信号控制、运输事故响应等要素的整合。
(4)以大型系统仿真软件Flexsim为研究平台,建立矿山运输系统仿真模型,实现区域矿山地下运输系统仿真。根据离散事件仿真理论,结合矿山地下运输系统模拟模型,以单轨、双轨运输模型单元构建各矿山子运输系统模型。进行子运输系统模型的合并得到区域运输系统的总体模型。根据模型研究在不同条件下运输系统的运行状况,评价运输系统运行效能,实现区域地下矿山运输系统决策的智能化和可视化。
(5)分析水资源系统混沌特征,在水资源安全领域中引入混沌理论,将混沌理论和神经网络相结合,建立相空间重构神经网络耦合的神经网络需水量预测模型。通过模型调试,参数误差与灵敏度分析,进行水资源预测模拟。通过对模拟值和历史统计数据的比较,以验证模型的有效性,反映个旧东区水资源承载力的特征及水资源系统状态的演化规律。
Designing and planning of mine construction is a strong integrity engineering include technology, economy, resource and environment, however, regional mine that make up of multi-mines put forward higher request on productivity configuration together with its development haulage system. On the basis of assets realignment, resources integration and construction of "Three Platform" of regional mine in Yunnan Tin Company, this paper focus on the study on multi-mines productivity configuration and regional development haulage system reconstruction and optimization. The research with a view to innovation of exploitation mode of resources and acceleration of great-leap-forward development of enterprise under the eidos of harmonious, unification and efficiency, which deeply analyze and discuss the problems such as optimization of selection and collection, optimization of development haulage system, virtual simulation of haulage system and safety prediction and optimized disposition of water resource by using modern non-linear scientific method and computer technology. The detailed research contents as follows:
(1) By using genetic algorithm (GA) optimizing the throughput of the concentration plant, and establishing a multi-objective optimization mathematic model of the concentrating plant. According to genetic algorithm theory, in combination with the essential of regional mine concentrating plant's throughput, which is multi-objective, multi-factor with continuous and discrete variables combinatorial optimization at the same time, taking the mine total ability of mineral processing as fitness, and establishing the objective function with the largest fitness. By optimizing design the choice of variables, developing transport systems engineering layout and the tailings storage capacity for the introduction of the main constraints, and use the best of the genetic algorithm search software to realize the solving of optimization mathematic model. The results of optimization supply the decision-making basis for adjusting the layout of mineral processing, improving the mineral processing ability structure of concentration plant, and realizing the maximum of resource utilization ratio and benefit.
(2) Constructing the planning and optimizing model of regional underground mine development system based on ant colony algorithm theory. Transforming the complicated regional mine development system optimization into a network flow, comprehensive application of the minimum cost maximum flow, the absolute center of the network flow theory to research the development system optimization of regional mine. Advancing the proposed regional system of underground mine development planning model based on the ant colony algorithm, achieving improve the computer simulation of ant colony algorithm, and the ant colony algorithm of regional underground mine development system.
(3) The paper analyzed the characteristic of underground mine development and transportation system, and expatiated the basic theory of computer simulation, and established the simulation model of underground mine transport system. Based on the discrete event system simulation, combining with stochastic time-series simulation recursive model to carry on the development and transport system simulation design, and the paper initially established each transport subsystem micro-model and the model of whole regional mine transport system. The simulation model includes functional modules and logic structure, achieving the integration of the character of transport network and train movement, transport signal control, transport accident response.
(4) The simulating model of transport system is established by using the large simulation software Flexsim to simulate regional mine underground transport system. According to the simulation theory of discrete events and mine underground transport system simulation model, setting up mine sub-transport system model with monorail and double transport model cell. Collectivity model of regional transport system is obtained by combining sub-transport system models. Studying on the operations of transport system at different situations in order to evaluate the operation effect and realize intelligent and visualization of decision-making.
(5) Analyzing the chaotic features of water resources system, by introducing the chaos theory into the field of water resources security combining with neural network to establish phase space reconstruction neural network coupled neural network model to predict water. By debugging model, analyzing parameter error and sensitivity to take on water resources forecasting simulation. Compare with the simulated values and historical of statistical data, verifying the validity of the model and reflecting the characteristics of water resources bearing capacity in the eastern of Gejiu and the state of the system evolution rule.
引文
[1]吴雪,赵艺.最大加权独立集问题的DNA算法[J].电子与信息学报,2007,29(11):2693-2697
[2]Bagley J D.The Behavior of Adaptive Systems Which Employ Genetic and Correlation Algorithms:[Ph D Dissertation].Unvirsity of Michigan,No,68-75,1967
[3]陈国良,王煦法,庄镇泉,等.遗传算法及其应用[M].北京:人民邮电出版社,2001
[4]Evolver:The genetic algorithm solver for Microsoft Excel 4.0.USA:Palisade Corporation,2001:184-186
[5]Daniel P R.Aircraft Design:A Conceptual Approach[M].Washington D C:AIAA.Ine,1989
[6]邹慧君,汪利,王石刚等.机械产品概念设计及其方法综述[J].机械设计与研究,1998,14(2):9-12.
[7]马安鹏,韩晓建,邓家.产品设计技术及其发展[J].航空制造技术,2000,(4):13-25
[8]Pahl G,Beitz W.Engineering Design[M].London:The Design Council,1984
[9]FrenehM J.Conceptual Design for Engineers Second Edition[M].London:The Design Council,1985
[10]赵少奎,杨永太著.工程系统工程导论[M].北京:国防工业出版社,2000
[11]许树柏.层次分析法原理[M].天津:天津大学出版社,1988
[12]王宗军.综合评价的方法问题及其研究趋势[J].管理科学学报,1998,1(1):73-79
[13]石守东,陈文培,陆品.层次分析法在运载器系统中的应用[J].船舶工程,2001,(2):28-30
[14]林庆国.层次分析法在空间推进系统综合评价中的应用[J].上海航天,2000,(1):19-23
[15]刘鸿恩,张列平.质量功能展开(QFD)理论与方法-研究进展综述[J].系统工程,2000,18(2):1-6
[16]林志航,车阿大.质量功能配置研究及进展--兼谈对我国QFD研究与应用的看法[J].机械科学与技术,1998,17(1):119-121
[17]宋笔锋.QFD方法在飞机综合设计中的应用[A].中国航空学会飞机总体专业年会[C],1999
[18]杨剑波.多目标决策方法与应用[M].长沙:湖南出版社,1996
[19]黄德才,郑河荣.理想点决策方法的逆序问题与逆序的消除[J].系统工程与电子技术,2001,23(12):80-83
[20]Michelle R Kirby,Dimitri N Mavris.A method for technology selection on benefit,available schedule and budget resources[R].AIAA-2000-5563
[21]Cartagena M A,Rosorio J E,Mavris D N.A method for technology identification,evaluation,and selection of aircraft propulsion systems[R].AIAA-2000-3712
[22]刘普寅,吴孟达.模糊理论及其应用[M].长沙:国防科技大学出版社,1998
[23]钟诗盛.工程方案设计中的模糊理论与技术[M].哈尔滨:哈尔滨工业大学出版社,2000
[24]韩晓建,邓家.产品概念设计的综合评价方法[J].北京航空航天大学学报,2000,26(2):210-212
[25]张吉军.模糊层次分析法(FAHP)[J].模糊系统与数学,2000,14(2):80-88.
[26]车阿大,林志航,方勇.模糊集理论在QFD中的应用[J].系统理论方法与应用,1998,7(2):55-62
[27]邓聚龙.灰色系统基本方法[M].武汉:华中工学院出版社,1987
[28]周前祥,张达贤.工程系统设计方案多目标灰色关联度决策模型及其应用研究[J].系统工程与电子技术,1999,21(1):1-7
[29]吕锋,崔晓辉.多目标决策灰色关联投影法及其应用[J].系统工程理论与实践,2002,22(1):103-107.
[30]顾基发.意见综合--怎样达成共识[J].系统工程学报,2001,16(5):340-348.
[31]樊治平,张权.群体多准则决策分析的TOPSIS算法[J].沈阳工业大学学报,1996,8(3):90-94.
[32]宋玉银,蔡复之,等.基于实例推理的产品概念设计系统[J].北京:清华大学学报(自然科学版),1998,38(8):5-8.
[33]Mawis D N,Kirby M R,Qiu S.Technology impact forecasting for a high speed transport[R],AIAA-98-5547
[34]Kirby M R.A Methodology for technology identification,evaluation,and selection in conceptual and preliminary aircraft design[D].Georgia Institute of Technology,2001
[35]Surya N Patnaik,Dail A Hopkins,Rula M.Coroneos,Aircraft engine preliminary design utilizing a cascade optimization strategy with neural network and aggression approximations[R],AIAA-2000-4766
[36]舒慧林,刘继红,钟毅芳.计算机辅助机械产品概念设计研究综述[J].计算机辅助设计与图形学学报,2000,12(2):47-954
[37]王林鸿,庞兴华,张金定.虚拟概念设计系统--概念设计方法的变革[J].机械设计与制造,1999,(4):71-72
[38]黄光球,桂中岳.地下矿开拓运输系统优化的遗传算法[J].有色矿冶,1997,(3):7-12
[39]云庆夏,黄凯明.确定地下矿开拓系统的遗传算法[J].金属矿山,2001,(2):16-19
[40]聂兴信,陈永锋,卢才武.遗传算法在露天矿开拓系统优化中的应用[J].金属矿山,2007,(2):19-22
[41]Bonabeau E,Dorigo M,Theraulaz G Inspiration for optimization fromm social insect behaviour.Nature,2000,406(6):39-42
[42]Marco Dorigo,Tomas Stutzle.Ant Colony Optimization.Cambridge,Masschusetts London,England.MIT Press,2004
[43]Colonri A,Dorigo M.Heuristics from nature for hard combinatorial optimization problems.International Tails Operational Research,1996,3(1):1-21.
[44]李士勇,陈永强,李研.蚁群算法及其应用[M].哈尔滨:哈尔滨工业大学出版社,2004
[45]Coloni A,Dorigo M and Maniezzo V.Ant system:Optimization by a colony of cooperating agent[J].IEEB Trans on Systems,Man and Cybernetics-Part B:Cybernetics.1996,vol,26(1):29-41
[46]M.Dorigo,L.M.Gambardella.A Study of Some Properties of Ant-Q.Proceedings of PPSN IV-Fourth International Conference on Parallel Problem Solving From Nature.1996:656-665P
[47]Bilchev G and Parmee I.C.Searching heavily constramed design spaces[A].In:Proc.Of 22nd Int.Conf.Computer Aided Desigh'95.Yelta:Ukrame,1995,230-235
[48]张纪会,徐心和.一种新的进化算法--蚁群算法[J].系统工程理论与实践,1999,(3):84-89
[49]高玮,郑颖人.蚁群算法及其在硐群施工优化中的应用[J].岩石力学与工程学报,2002,2l(4):471-474
[50]Gambardella,L.M.,Dorigo,M.HAS-SOP:an hybrid ant system for the sequential ordering problem.Technique Report,No.IDSIA 97-11,IDSIA,Lugano,Switzerland,1997
[51]张纪会,高齐圣,徐心和.自适应蚁群算法[J].控制理论与应用,2000,17(1):1-5
[52]陈烨.带杂交算子的蚁群算法[J].计算机工程,2001,27(12):74-77
[53]黄凯明.遗传算法在开拓系统结构优化中的应用[硕士学位论文].西安:西安建筑科技大,2002
[54]Boesel J,Bowden Jr R,Glover F,etal.Future of simulation optimization[A].Proc Winter Simulation Conference[C].Arlington,2001.1466-1469
[55]Azadivar F.Simulation optimization methodologies[A].Proc Winter Simulation Conference[C].Phoenix,1999.93-100
[56]Glynn P W.Likelihood ratio derivative estimation for stochastic systems[A].Proc Winter Simulation Conference[C].San Diego,1989:374-380
[57]Ho Y C,Cao X R.Perturbation Analysis of Discrete Event Dynamic Systems[M].Boston:Kluwer,1991
[58]Morrice D J,Schruben L W.Simulation sensitivity analysis using frequency domain experiments[A].P roc Winter Simulation Conference[C].San Diego,1989:367-373.
[59]Shapiro A.Simulation based optimization[A].Proc Winter Simulation Conference [C].San Diego,1996:332-336
[60]Wilson J R.Future direction in response surface methodology for simulation[A].Proc Winter Simulation Conference[C].San Diego,1987:378-381
[6l]王凌,郑大钟.Meta-heurstic算法研究进展[J].控制与决策,2000,15(3):257-262
[62]Kirkpatrick S,Gelatt C D,Vecchi M P.Optimization by simulated annealing[J].Science,1983,220(4598):671-680
[63]Fogel D B.Evolutionary Computation:The Fossil Record[M].New York:IEEE Press,1998
[64]Glover F,Laguna M.Tabu Search[M].Boston,MA:Kluwer Academic Publisher,1997
[65]Shi L,Olafsson S.Nested partition method for global optimization[J].Operations Research,2000,48(3):390-407
[66]Nelder J A,Mead R.A simplex method for function minimization[J].Computer J,1965,7(2):308-313
[67]Gupta S S,Panchapakesan S.Multiple-decision Procedures-theory and Methodology of S election and Ranking Populations[M].New York:Wiley,1979
[68]Ho Y C,Cassandras C G,Chen C H,et al.Ordinal optimization and simulation[J].J of Operations Research Society,2000,51(4):490-500
[69]Dai L Y.Convergence property of ordinal comparison in the simulation of discrete event dynamic systems[J].Production Planning and Control,1996,7(6):554-565
[70]Saltenis V,Tiesis V.Simulation and optimization of radar search strategies[J].Informatica,1992,3(2):256-274
[71]Chen M C,Tsai D M.Simulation optimization through direct search for multi-objective manufacturing system s[J].Production Planning and Control,1996,7(6):554-565
[72]Haddock J,Mittenthal J.Simulation optimization using simulated annealing[J].Computers and Industrial Engineering,1992,22(4):387-395
[73]Ahmed M A,Alkham is T M,Hasan M.Optimizing discrete stochastic systems using simulated annealing and simulat ion[J].Computers and Industrial Engineering,1997,32(4):823-836
[74]Javor A,Szucs G.Simulation and operation of urban traffic using AI[J].Mathematics and Computers in Simulation,1998,46(1):1321
[75]Consiglio A,Zenios S A.Designing portfolios offinancial products via integrated simulation and optimization models[J].Operation Research,1999,47(2):195-208
[76]Kleinman N L,Spall J C,Naiman D Q.Simulation-based optimization with stochastic approximation using common random numbers[J].Management Science,1999,45(11):1570-1578
[77]Shyam sundar V,Rangaiah G P.A method for simulation optimization of multiphase distillation[J].Computers and Chemical Engineering,2000,24(1):23-37
[78]Paris J L,Pierreval H.A distributed evolutionary simulation optimization approach for the configuration of multiproduct Kanban systems[J].Int J C IM,2001,14(5):421-430
[79]Pierreval H,Paris J L.Distributed evolutionary algorithms for simulation optimization[J].IEEE Trans SM C-A,2000,30(1):15-24
[80]Pfeiffer F,Rossmann T,Bolotnik N N,et al.Simulation optimization of regularmotions of a tube-crow ling robot[J].Multibody System Dynamics,2001,5(2):159-184
[81]Azadivar F,Tomkins G.Simulation optimization with qualitative variables and structural model changes:A genetic algo rithm approach[J].European J of Operational R esearch,1999,113(1):169-182
[82]Gurkan G.Simulation optimization of buffer allocations in production lines with unreliable machines[J].Annals of Operations Research,2000,93(1/4):177-216
[83]Greenwood A G,Ree L P,Siochi F C.Aninvestigation of the behaviour of simulation response surfaces[J].European J of Operational Research,1998,110(2):282-313
[84]Barton R R,Ivey JS Jr.Nelder-M cad simplex modifications for simulation optimization[J].Management Science,1996,42(7):954-973
[85]Humphrey D G,Wilson J R.A revised simplex search procedure for stochastic simulation response surface optimization[J].IN FORM S J Computing,2000,12(4):272-283
[86]Ree L P,Greenwood A G,Sioch i F C.A best-first search approach for determining starting regions in simulation optimization[J].lEE Trans,2002,34(3):283-295
[87]Wang L,Zheng D Z.An effective hybrid optimization strategy for job-shop scheduling problems[J].Computers and Operations Research,2001,28(6):585-596
[88]王凌,李文峰,郑大钟.基于一类混合策略的模型参数估计和控制器参数整定研究[J].控制与决策,2001,16(5):530-534
[89]Khoo L P,Chen C H.Integration of response surface methodology with genetic algorithms[J].Int J Advanced Manufacturing Technology,2001,18(7):483-489
[90]Yucesan E,Luo Y C,Chen C H,etal.Distributed web-based simulation experiments for optimization[J].Simulation Practice and Theory,2001,9(12):73-90
[91]Chen H C,Chen C H,Yucesan E.Computing efforts allocation for ordinal optimization and discrete event simulation[J].IEEE Trans Automatic Control,2000,45(5):960-964
[92]Havlik I,Schorcht R.MOPSI/CM:A program system for simulation and optimization of dynamic systems with combined models[J].Systems Analysis Modelling Simulation,1990,7(8):637-647
[93]Bengu G,Haddock J.An implementation of a simulation optimization system[J].Int J Computer Simulation,1994,4(3):305-325
[94]Behe R,Bills R,Brachat P,etal.Simulation and optimization software for axisymmetrical radiating structures[J].Annals of Telecommunications,1994,49(9/10):575-588
[95]Dote Y,Ovaska S J.Industrial applications of soft computing:A review[J].Proc of IEEE,2001,89(9):1243-1265
[96]王凌,郑大钟.混合优化策略统一结构的探讨[J].控制与决策,2002,17(1):33-36
[97]古德生.地下金属矿采矿科学技术的发展趋势[J].黄金,2004,25(1):18-22.
[98]Wolverine Sofeware Corporation,Using Proof Animation,1995
[99]V.Koyukh,S.Galiyev,and Z.Li,Mine simulation in Asia,Internatinal journal of Surface Mining,Reclamatin and Environment,1999,13(2)
[100]D.R.卡拉斯基,J.M.穆特曼斯基.矿业中的计算机模拟:国际动态分析[J].国外金属矿山,1998,(2):63-68
[101]黄德才,郑河荣.理想点决策方法的逆序问题与逆序的消除[J].系统工程与电子技术,2001,23(12):80-83
[102]T.Turk,System dynamics simulation of computer networks:Price-controlled QoS framework,Math.Comput.Simul.(2007)
[103]Leonardo Chwif,Ray J.Paul,Marcos Ribeiro Pereira Barretto.Discrete event simulation model reduction:A causal approach,Simulation Modelling Practice and Theory 14(2006)930-944
[104]舒少龙,林峰,黄志强.离散事件系统N步稳定性分析[J].计算机工程与应用,2007,43(7):17-19
[105]刘澜.客运专线列车速度控制方式设计的优化模型及应用[J].西南交通大学学报,2000,35(3):280-283.
[106]刘鑫元,刘云.铁路智能运输系统(RITS)网络结构设计[J].铁道通信信号,2004,40(12):19-21
[107]詹跃东,骆瑛.基于Petri网的物流自动化系统建模与仿真研究[J].系统仿真学报,2001,13(4):501-504
[108]唐孝飞.第三方物流配送仿真系统的研究[J].系统仿真学报,2002,14(1):44-47
[109]George N.,Panagiotou,Discrete mine system simulation in Europe,Internatinal journal of Surface Mining,Reelamatin and Environment,1999,13(2)
[110]常鹏.基于Flexsim的散货码头物流系统仿真[J].中国水运(理论版),2007,5(4):156-157
[111]张卫德.基于Flexsim的生产线仿真和应用[J].工业控制计算机,2005,18(9):46-47
[112]薛家兵.基于Flexsim仿真的FMS车间级控制系统开发[J].中国制造业信息化,2007,36(1):38-41
[113]刘利军.基于仿真的自动化立体仓库运行效率研究[J].煤矿机械,2006,27(10):66-68
[114]张晓萍.物流系统仿真原理与应用[M].北京:中国物资出版社,2005
[115]王慧敏,佟金萍,马小平,等.基于CAS范式的流域水资源配置与管理及建模仿真[J].系统工程理论与实践,2005,(12):118-124
[116]丁涛,周惠成.混沌时间序列局域预测方法[J].系统工程与电子技术,2004,26(3):338-340
[117]吕金虎,张锁春.加权一阶局域法在电力系统短期负荷预测中的应用[J].控制理论与应用,2002,19(5):767-770
[118]甘建超,肖先赐.基于相空间领域的混沌时间序列自适应预测滤波器(1)非线性自适应滤波[J].物理学报,2003,52(5):1096-1101
[119]甘建超,肖先赐.基于相空间领域的混沌时间序列自适应预测滤波器(2)非线性自适应滤波[J].物理学报,2003,52(5):1102-1107
[120]吕金虎等.混沌时间序列分析及其应用[M].武汉:武汉大学出版社,2002
[121]向小东.基于神经网络与混沌理论的非线性时间序列预测研究[D].西南交通大学,2001
[122]丁胜祥,董增川,王德智,等.基于相空间重构和GA-BP网络德径流预测[J].水电能源科学,2007,25(5):40-43
[123]王妍,徐伟,曲继圣.基于时间序列的相空间重构算法及验证(一)[J].山东大学学报(工学版),2005,35(4):109-111
[124]Packard N H.Crutchfield J P.Famer J D,et al.Geometry from a Time Series[J].Phy.Rev.Lett,1980,45(9):712-716
[125]Takens F.Dynamical systems and turbulence.In:Rand D,Young L S,eds.Lecture Notes in Mathematics.Berlin:Springer,1981:366-381
[126]Tankens F Mane.Detecting strange attractors in fluid turbulence[C].Dynamical Systems and Turbulence[C].Berlin:Springer,1981:21-80
[127]Bezruchko B P,Karavaev A S,Ponomarenko C I,et al.Reconstruction of time-delay systems from chaotic time series[J].Phys Rev E,2001,64,1-3
[128]王海燕,盛昭瀚.混沌时间序列相空间重构参数的选取方法[J].东南大学学报(自然科学版),2000,30(5):113-117
[2]Bagley J D.The Behavior of Adaptive Systems Which Employ Genetic and Correlation Algorithms:[Ph D Dissertation].Unvirsity of Michigan,No,68-75,1967
[3]陈国良,王煦法,庄镇泉,等.遗传算法及其应用[M].北京:人民邮电出版社,2001
[4]Evolver:The genetic algorithm solver for Microsoft Excel 4.0.USA:Palisade Corporation,2001:184-186
[5]Daniel P R.Aircraft Design:A Conceptual Approach[M].Washington D C:AIAA.Ine,1989
[6]邹慧君,汪利,王石刚等.机械产品概念设计及其方法综述[J].机械设计与研究,1998,14(2):9-12.
[7]马安鹏,韩晓建,邓家.产品设计技术及其发展[J].航空制造技术,2000,(4):13-25
[8]Pahl G,Beitz W.Engineering Design[M].London:The Design Council,1984
[9]FrenehM J.Conceptual Design for Engineers Second Edition[M].London:The Design Council,1985
[10]赵少奎,杨永太著.工程系统工程导论[M].北京:国防工业出版社,2000
[11]许树柏.层次分析法原理[M].天津:天津大学出版社,1988
[12]王宗军.综合评价的方法问题及其研究趋势[J].管理科学学报,1998,1(1):73-79
[13]石守东,陈文培,陆品.层次分析法在运载器系统中的应用[J].船舶工程,2001,(2):28-30
[14]林庆国.层次分析法在空间推进系统综合评价中的应用[J].上海航天,2000,(1):19-23
[15]刘鸿恩,张列平.质量功能展开(QFD)理论与方法-研究进展综述[J].系统工程,2000,18(2):1-6
[16]林志航,车阿大.质量功能配置研究及进展--兼谈对我国QFD研究与应用的看法[J].机械科学与技术,1998,17(1):119-121
[17]宋笔锋.QFD方法在飞机综合设计中的应用[A].中国航空学会飞机总体专业年会[C],1999
[18]杨剑波.多目标决策方法与应用[M].长沙:湖南出版社,1996
[19]黄德才,郑河荣.理想点决策方法的逆序问题与逆序的消除[J].系统工程与电子技术,2001,23(12):80-83
[20]Michelle R Kirby,Dimitri N Mavris.A method for technology selection on benefit,available schedule and budget resources[R].AIAA-2000-5563
[21]Cartagena M A,Rosorio J E,Mavris D N.A method for technology identification,evaluation,and selection of aircraft propulsion systems[R].AIAA-2000-3712
[22]刘普寅,吴孟达.模糊理论及其应用[M].长沙:国防科技大学出版社,1998
[23]钟诗盛.工程方案设计中的模糊理论与技术[M].哈尔滨:哈尔滨工业大学出版社,2000
[24]韩晓建,邓家.产品概念设计的综合评价方法[J].北京航空航天大学学报,2000,26(2):210-212
[25]张吉军.模糊层次分析法(FAHP)[J].模糊系统与数学,2000,14(2):80-88.
[26]车阿大,林志航,方勇.模糊集理论在QFD中的应用[J].系统理论方法与应用,1998,7(2):55-62
[27]邓聚龙.灰色系统基本方法[M].武汉:华中工学院出版社,1987
[28]周前祥,张达贤.工程系统设计方案多目标灰色关联度决策模型及其应用研究[J].系统工程与电子技术,1999,21(1):1-7
[29]吕锋,崔晓辉.多目标决策灰色关联投影法及其应用[J].系统工程理论与实践,2002,22(1):103-107.
[30]顾基发.意见综合--怎样达成共识[J].系统工程学报,2001,16(5):340-348.
[31]樊治平,张权.群体多准则决策分析的TOPSIS算法[J].沈阳工业大学学报,1996,8(3):90-94.
[32]宋玉银,蔡复之,等.基于实例推理的产品概念设计系统[J].北京:清华大学学报(自然科学版),1998,38(8):5-8.
[33]Mawis D N,Kirby M R,Qiu S.Technology impact forecasting for a high speed transport[R],AIAA-98-5547
[34]Kirby M R.A Methodology for technology identification,evaluation,and selection in conceptual and preliminary aircraft design[D].Georgia Institute of Technology,2001
[35]Surya N Patnaik,Dail A Hopkins,Rula M.Coroneos,Aircraft engine preliminary design utilizing a cascade optimization strategy with neural network and aggression approximations[R],AIAA-2000-4766
[36]舒慧林,刘继红,钟毅芳.计算机辅助机械产品概念设计研究综述[J].计算机辅助设计与图形学学报,2000,12(2):47-954
[37]王林鸿,庞兴华,张金定.虚拟概念设计系统--概念设计方法的变革[J].机械设计与制造,1999,(4):71-72
[38]黄光球,桂中岳.地下矿开拓运输系统优化的遗传算法[J].有色矿冶,1997,(3):7-12
[39]云庆夏,黄凯明.确定地下矿开拓系统的遗传算法[J].金属矿山,2001,(2):16-19
[40]聂兴信,陈永锋,卢才武.遗传算法在露天矿开拓系统优化中的应用[J].金属矿山,2007,(2):19-22
[41]Bonabeau E,Dorigo M,Theraulaz G Inspiration for optimization fromm social insect behaviour.Nature,2000,406(6):39-42
[42]Marco Dorigo,Tomas Stutzle.Ant Colony Optimization.Cambridge,Masschusetts London,England.MIT Press,2004
[43]Colonri A,Dorigo M.Heuristics from nature for hard combinatorial optimization problems.International Tails Operational Research,1996,3(1):1-21.
[44]李士勇,陈永强,李研.蚁群算法及其应用[M].哈尔滨:哈尔滨工业大学出版社,2004
[45]Coloni A,Dorigo M and Maniezzo V.Ant system:Optimization by a colony of cooperating agent[J].IEEB Trans on Systems,Man and Cybernetics-Part B:Cybernetics.1996,vol,26(1):29-41
[46]M.Dorigo,L.M.Gambardella.A Study of Some Properties of Ant-Q.Proceedings of PPSN IV-Fourth International Conference on Parallel Problem Solving From Nature.1996:656-665P
[47]Bilchev G and Parmee I.C.Searching heavily constramed design spaces[A].In:Proc.Of 22nd Int.Conf.Computer Aided Desigh'95.Yelta:Ukrame,1995,230-235
[48]张纪会,徐心和.一种新的进化算法--蚁群算法[J].系统工程理论与实践,1999,(3):84-89
[49]高玮,郑颖人.蚁群算法及其在硐群施工优化中的应用[J].岩石力学与工程学报,2002,2l(4):471-474
[50]Gambardella,L.M.,Dorigo,M.HAS-SOP:an hybrid ant system for the sequential ordering problem.Technique Report,No.IDSIA 97-11,IDSIA,Lugano,Switzerland,1997
[51]张纪会,高齐圣,徐心和.自适应蚁群算法[J].控制理论与应用,2000,17(1):1-5
[52]陈烨.带杂交算子的蚁群算法[J].计算机工程,2001,27(12):74-77
[53]黄凯明.遗传算法在开拓系统结构优化中的应用[硕士学位论文].西安:西安建筑科技大,2002
[54]Boesel J,Bowden Jr R,Glover F,etal.Future of simulation optimization[A].Proc Winter Simulation Conference[C].Arlington,2001.1466-1469
[55]Azadivar F.Simulation optimization methodologies[A].Proc Winter Simulation Conference[C].Phoenix,1999.93-100
[56]Glynn P W.Likelihood ratio derivative estimation for stochastic systems[A].Proc Winter Simulation Conference[C].San Diego,1989:374-380
[57]Ho Y C,Cao X R.Perturbation Analysis of Discrete Event Dynamic Systems[M].Boston:Kluwer,1991
[58]Morrice D J,Schruben L W.Simulation sensitivity analysis using frequency domain experiments[A].P roc Winter Simulation Conference[C].San Diego,1989:367-373.
[59]Shapiro A.Simulation based optimization[A].Proc Winter Simulation Conference [C].San Diego,1996:332-336
[60]Wilson J R.Future direction in response surface methodology for simulation[A].Proc Winter Simulation Conference[C].San Diego,1987:378-381
[6l]王凌,郑大钟.Meta-heurstic算法研究进展[J].控制与决策,2000,15(3):257-262
[62]Kirkpatrick S,Gelatt C D,Vecchi M P.Optimization by simulated annealing[J].Science,1983,220(4598):671-680
[63]Fogel D B.Evolutionary Computation:The Fossil Record[M].New York:IEEE Press,1998
[64]Glover F,Laguna M.Tabu Search[M].Boston,MA:Kluwer Academic Publisher,1997
[65]Shi L,Olafsson S.Nested partition method for global optimization[J].Operations Research,2000,48(3):390-407
[66]Nelder J A,Mead R.A simplex method for function minimization[J].Computer J,1965,7(2):308-313
[67]Gupta S S,Panchapakesan S.Multiple-decision Procedures-theory and Methodology of S election and Ranking Populations[M].New York:Wiley,1979
[68]Ho Y C,Cassandras C G,Chen C H,et al.Ordinal optimization and simulation[J].J of Operations Research Society,2000,51(4):490-500
[69]Dai L Y.Convergence property of ordinal comparison in the simulation of discrete event dynamic systems[J].Production Planning and Control,1996,7(6):554-565
[70]Saltenis V,Tiesis V.Simulation and optimization of radar search strategies[J].Informatica,1992,3(2):256-274
[71]Chen M C,Tsai D M.Simulation optimization through direct search for multi-objective manufacturing system s[J].Production Planning and Control,1996,7(6):554-565
[72]Haddock J,Mittenthal J.Simulation optimization using simulated annealing[J].Computers and Industrial Engineering,1992,22(4):387-395
[73]Ahmed M A,Alkham is T M,Hasan M.Optimizing discrete stochastic systems using simulated annealing and simulat ion[J].Computers and Industrial Engineering,1997,32(4):823-836
[74]Javor A,Szucs G.Simulation and operation of urban traffic using AI[J].Mathematics and Computers in Simulation,1998,46(1):1321
[75]Consiglio A,Zenios S A.Designing portfolios offinancial products via integrated simulation and optimization models[J].Operation Research,1999,47(2):195-208
[76]Kleinman N L,Spall J C,Naiman D Q.Simulation-based optimization with stochastic approximation using common random numbers[J].Management Science,1999,45(11):1570-1578
[77]Shyam sundar V,Rangaiah G P.A method for simulation optimization of multiphase distillation[J].Computers and Chemical Engineering,2000,24(1):23-37
[78]Paris J L,Pierreval H.A distributed evolutionary simulation optimization approach for the configuration of multiproduct Kanban systems[J].Int J C IM,2001,14(5):421-430
[79]Pierreval H,Paris J L.Distributed evolutionary algorithms for simulation optimization[J].IEEE Trans SM C-A,2000,30(1):15-24
[80]Pfeiffer F,Rossmann T,Bolotnik N N,et al.Simulation optimization of regularmotions of a tube-crow ling robot[J].Multibody System Dynamics,2001,5(2):159-184
[81]Azadivar F,Tomkins G.Simulation optimization with qualitative variables and structural model changes:A genetic algo rithm approach[J].European J of Operational R esearch,1999,113(1):169-182
[82]Gurkan G.Simulation optimization of buffer allocations in production lines with unreliable machines[J].Annals of Operations Research,2000,93(1/4):177-216
[83]Greenwood A G,Ree L P,Siochi F C.Aninvestigation of the behaviour of simulation response surfaces[J].European J of Operational Research,1998,110(2):282-313
[84]Barton R R,Ivey JS Jr.Nelder-M cad simplex modifications for simulation optimization[J].Management Science,1996,42(7):954-973
[85]Humphrey D G,Wilson J R.A revised simplex search procedure for stochastic simulation response surface optimization[J].IN FORM S J Computing,2000,12(4):272-283
[86]Ree L P,Greenwood A G,Sioch i F C.A best-first search approach for determining starting regions in simulation optimization[J].lEE Trans,2002,34(3):283-295
[87]Wang L,Zheng D Z.An effective hybrid optimization strategy for job-shop scheduling problems[J].Computers and Operations Research,2001,28(6):585-596
[88]王凌,李文峰,郑大钟.基于一类混合策略的模型参数估计和控制器参数整定研究[J].控制与决策,2001,16(5):530-534
[89]Khoo L P,Chen C H.Integration of response surface methodology with genetic algorithms[J].Int J Advanced Manufacturing Technology,2001,18(7):483-489
[90]Yucesan E,Luo Y C,Chen C H,etal.Distributed web-based simulation experiments for optimization[J].Simulation Practice and Theory,2001,9(12):73-90
[91]Chen H C,Chen C H,Yucesan E.Computing efforts allocation for ordinal optimization and discrete event simulation[J].IEEE Trans Automatic Control,2000,45(5):960-964
[92]Havlik I,Schorcht R.MOPSI/CM:A program system for simulation and optimization of dynamic systems with combined models[J].Systems Analysis Modelling Simulation,1990,7(8):637-647
[93]Bengu G,Haddock J.An implementation of a simulation optimization system[J].Int J Computer Simulation,1994,4(3):305-325
[94]Behe R,Bills R,Brachat P,etal.Simulation and optimization software for axisymmetrical radiating structures[J].Annals of Telecommunications,1994,49(9/10):575-588
[95]Dote Y,Ovaska S J.Industrial applications of soft computing:A review[J].Proc of IEEE,2001,89(9):1243-1265
[96]王凌,郑大钟.混合优化策略统一结构的探讨[J].控制与决策,2002,17(1):33-36
[97]古德生.地下金属矿采矿科学技术的发展趋势[J].黄金,2004,25(1):18-22.
[98]Wolverine Sofeware Corporation,Using Proof Animation,1995
[99]V.Koyukh,S.Galiyev,and Z.Li,Mine simulation in Asia,Internatinal journal of Surface Mining,Reclamatin and Environment,1999,13(2)
[100]D.R.卡拉斯基,J.M.穆特曼斯基.矿业中的计算机模拟:国际动态分析[J].国外金属矿山,1998,(2):63-68
[101]黄德才,郑河荣.理想点决策方法的逆序问题与逆序的消除[J].系统工程与电子技术,2001,23(12):80-83
[102]T.Turk,System dynamics simulation of computer networks:Price-controlled QoS framework,Math.Comput.Simul.(2007)
[103]Leonardo Chwif,Ray J.Paul,Marcos Ribeiro Pereira Barretto.Discrete event simulation model reduction:A causal approach,Simulation Modelling Practice and Theory 14(2006)930-944
[104]舒少龙,林峰,黄志强.离散事件系统N步稳定性分析[J].计算机工程与应用,2007,43(7):17-19
[105]刘澜.客运专线列车速度控制方式设计的优化模型及应用[J].西南交通大学学报,2000,35(3):280-283.
[106]刘鑫元,刘云.铁路智能运输系统(RITS)网络结构设计[J].铁道通信信号,2004,40(12):19-21
[107]詹跃东,骆瑛.基于Petri网的物流自动化系统建模与仿真研究[J].系统仿真学报,2001,13(4):501-504
[108]唐孝飞.第三方物流配送仿真系统的研究[J].系统仿真学报,2002,14(1):44-47
[109]George N.,Panagiotou,Discrete mine system simulation in Europe,Internatinal journal of Surface Mining,Reelamatin and Environment,1999,13(2)
[110]常鹏.基于Flexsim的散货码头物流系统仿真[J].中国水运(理论版),2007,5(4):156-157
[111]张卫德.基于Flexsim的生产线仿真和应用[J].工业控制计算机,2005,18(9):46-47
[112]薛家兵.基于Flexsim仿真的FMS车间级控制系统开发[J].中国制造业信息化,2007,36(1):38-41
[113]刘利军.基于仿真的自动化立体仓库运行效率研究[J].煤矿机械,2006,27(10):66-68
[114]张晓萍.物流系统仿真原理与应用[M].北京:中国物资出版社,2005
[115]王慧敏,佟金萍,马小平,等.基于CAS范式的流域水资源配置与管理及建模仿真[J].系统工程理论与实践,2005,(12):118-124
[116]丁涛,周惠成.混沌时间序列局域预测方法[J].系统工程与电子技术,2004,26(3):338-340
[117]吕金虎,张锁春.加权一阶局域法在电力系统短期负荷预测中的应用[J].控制理论与应用,2002,19(5):767-770
[118]甘建超,肖先赐.基于相空间领域的混沌时间序列自适应预测滤波器(1)非线性自适应滤波[J].物理学报,2003,52(5):1096-1101
[119]甘建超,肖先赐.基于相空间领域的混沌时间序列自适应预测滤波器(2)非线性自适应滤波[J].物理学报,2003,52(5):1102-1107
[120]吕金虎等.混沌时间序列分析及其应用[M].武汉:武汉大学出版社,2002
[121]向小东.基于神经网络与混沌理论的非线性时间序列预测研究[D].西南交通大学,2001
[122]丁胜祥,董增川,王德智,等.基于相空间重构和GA-BP网络德径流预测[J].水电能源科学,2007,25(5):40-43
[123]王妍,徐伟,曲继圣.基于时间序列的相空间重构算法及验证(一)[J].山东大学学报(工学版),2005,35(4):109-111
[124]Packard N H.Crutchfield J P.Famer J D,et al.Geometry from a Time Series[J].Phy.Rev.Lett,1980,45(9):712-716
[125]Takens F.Dynamical systems and turbulence.In:Rand D,Young L S,eds.Lecture Notes in Mathematics.Berlin:Springer,1981:366-381
[126]Tankens F Mane.Detecting strange attractors in fluid turbulence[C].Dynamical Systems and Turbulence[C].Berlin:Springer,1981:21-80
[127]Bezruchko B P,Karavaev A S,Ponomarenko C I,et al.Reconstruction of time-delay systems from chaotic time series[J].Phys Rev E,2001,64,1-3
[128]王海燕,盛昭瀚.混沌时间序列相空间重构参数的选取方法[J].东南大学学报(自然科学版),2000,30(5):113-117