飞机机票系统
网上机票预订系统,里面有详细的外部接口需求、性能需求、软件属性需求、数据需求、概念设计以及详细设计的部分代码相关内容,如果满意可以点击订票,把相关信息添加到机票数据库表中,如果不满意,可以点重置,所有信息清空,再重新选择。●退票窗口,用户可以根据用户信息表中的我的机票信息査询,找出机票号,在输入到机票号査询里,点击査询获得你的杋票信息以及价格显示,点击退票则在数据库机票信息表中朋除本条信息。●管理员界血的管理员添加芥血能对管理员信息进行査询、添加、删除和修改,包括用户名、登陆密码和联系方式。管理员界面的舱位信息的査询、添加、删除和修改,包括舱位等缴编号、舱位等级名称、提供的各种服务类别,以及备注信息等。●管理员界面的客机信息界面对客杋信息的添加、修改、删除和査询,包括客机编号、客机型号、购买时间、服役时间、经济舱座位数量、公务舱座位数量、头等舱座位数量以及备注信息等。●管理员界面的航线信息界面对航线信息的添加、修改、朋除和査询,包括航线编号、出发城市、到达城市、航班日期、出发时间、到达时间、客机编号、经济舱价格公务舱价格、头等舱价袼和备注信息等。●客户等级信息的的添加、修改、删除和查询,包括客户等级编号、客户等级名称折扣比例和备注信息等。管理员界面的用户信息查询,能查询所有的已注册的用户信息。管理员界面订票信息界面,可以査询所有的已订的机票的信息。23用户特点及一般约束1.管理员和用户的登陆要求合法的管理员以及用户才能登陆体统,防止系统被无关人员动用,使用字符串匹配对用户名和密码进行判断。管理员和用户的登录对相应的操作权限也不一样,如果是用户登录,管理员的窗口属性为不可用,管员冇舱位信息管,客杋信息管理,航线信息管理客户类型信息管理,客户信息和订票信息管理,用户主要有舱位査询,客机信息査询,航线信息查询,客户类型信息查询,自己的信息管理,和自己订票和退票2管理员的日常操作管理员可以对舱位信息,客杋信息,航线信息,客户类型信息,进行査询、修改和删除操作,可以对客户信息和订票信息只有査询操作。3用户的的日常操作用户可以进行注册然后登陆系统,可以查询舱位信息,客机信息査询,航线信息查询,客户类型信息,可以对自己的信息进行修改,可以定票(按照肮班号进行预订机票,显示所订机票的信息和价格,进入支付系统付账,并再次要求客户确认,确认无误后订票成功)可以退票(在特殊情况下(如天气不适合飞机起降,飞机延误超过30分钟)等给予全额退票,如果是个人原因只能退50%),对退票后的机票要在未售出机票中重新体现24功能需求(用DFD图表示)241用户注册的DFD图顶层图:点击新用填写信息P获得确认注册成功可户注册填写相应的登录信息注册图点击新用写信息PIP2获得确认户注册填写相应的返回用户注册成功信息注册信息D添加用户信息数据流图开输入用广名和密码而断用P名和测码是青正提错忍录金理面判断管理阻标志录时f结束242用户订票的DFD图顶层图用户登杳阅积订1层图PLP2用广登输入要查查洵机处理订订票订票成询D1机票信2层图D2订票信息票信息票信息P用户信息用户登陆处理用户查询用户查询订票信D3机票已卖完节息信息用户信息用户证信检查机票是D4订票有误信D1用户信息否存在机票数冒已满信息错更新机票误信息书信信用户243退票的DFD图顶层图白票信息用户登陆退票退票成功1层图D订票记用户登处理订票取消机票退票成机票信退款处退票的原因D2退票的相关规244机票信息查询的DFD图顶层图用户登的机票铜查询机机票层图:P用户脊找机处理机票机.票信机单D票信息清D2机票信25外部接口需求在用户界面方面要求错误信息格式均以弹出提示框的形式出现,硬软件接∏方面没有特别的需求,一般用户都可以直接使用。25性能需求因为系统夲身较小,并不投入实际应用,因此响应时间、结果精度方面可能会比较差,数据量大小方面能够处理较大的数据量。26软件属性需求在数据检索、数据增删改方面必须做到丝亳不差,满足软件开发的正确性要求。必须考虑充足的异常处哩机制以及软件的复用性,以便增强软件的健壮性。在安仝保密性方面倣到不同身份所能处理的事务不同,避免保密数据泄漏:设置足够的触发器对不安全的数据修改进行回滚操作,进而保证了安全性要求。所廾发出来的软件必须是叮维护的,不能把一些东西做的太妣。27数据需求(ER图表示)管理员信息实体E-R图如图2-1所示。管理员信息实体管理员编号管理员电话管理员用户名管理员密码舱位等级信息实体E-R图如图22所小。船位感位等线号m>均日图2-3客机信息实体ER图航线信息实体ER图如图2-4所示信比号备思出发城市到达球市图2-4航线信息实体ER图客户类型信息实体ER图如图2-5所示癣型实斗鬥型端号客广型姓名折比例图25客户类型信息实体ER图客户信息实体ER图如图2-6所示。吞户信实售户号客姓名联票图2-6客户信息实休ER图订票信息实体ER图如图2-7所示订票信息实体订票倌息号客户共客户信恩航线信息图2-7订票信息实体ER图实体之间关系的ER图如图2-8所示。舱位等信息机信容户垄型信客户记线设置户信息航线信息顸订机票□订哪信图28实体之间关系的ER图三、概要设计3.1总体设计(系统总流程图)网上机票预订系統用理路录广|需共●验证登陆名密码,正确进入主菜单,根据登录时所选的脊录方式(客户、管理员)的不同分别对用户设定不同的访问权限(如果是输入的客户用户名和密码正确,选择以客户方式登陆则主界面里面的管理员界面不能用,如果输入的是管理员的相应用户密码正确,以管理员的方式登陆则管珥员界面可用)不正确则清空登录框,最多可以输入三次,三次不止确系统会自动关闭登陆界面FrmLoFan用户名:Wg=icha距码:客尸登阵地定●新用户注册,新用户可以注册,注册时输入用户名可以杏询用户可不π用,可用就可以注册,注册时可以判断用户输入的密码和验证密码是否相同,相同才给以注册,如果满意可以点注册,注册成功后用户可以选择不用在回到登陆界面,可以直接陆到用户主界面,以后就可以用这个用户登录了,如果不满意,点取消,所有信息清空,重新输入注册界面Farm⊥Teg1tcrx用户注册1个人信启垆写用户名: ansi chao校验是古有重名操示长用片名由家字和导母组前A已1q位密码确认:**两输入的密码应一瘿姓名:思超性别:黑身份证号:同502419809029313庭址;太原迎新街联方式:卩3468832247输入正确的于机号电子信箱:i≤i2203183.cm我是一个开的人个人奋注:据不内提亮
- 2020-12-01下载
- 积分:1
Lectures on Stochastic Programming-Model
这是一本关于随机规划比较全面的书!比较难,不太容易啃,但是读了之后收获很大。这是高清版的!To Julia, Benjamin, Daniel, Nalan, and Yael;to Tsonka Konstatin and Marekand to the memory of feliks, Maria, and dentcho2009/8/20pagContentsList of notationserace1 Stochastic Programming ModelsIntroduction1.2 Invento1.2.1The news vendor problem1.2.2Constraints12.3Multistage modelsMultiproduct assembl1.3.1Two-Stage Model1.3.2Chance Constrained ModeMultistage modelPortfolio selection131.4.1Static model14.2Multistage Portfolio selection14.3Decision rule211.5 Supply Chain Network Design22Exercises2 Two-Stage Problems272.1 Linear Two-Stage Problems2.1.1Basic pi272.1.2The Expected Recourse Cost for Discrete Distributions 302.1.3The Expected Recourse Cost for General Distributions.. 322.1.4Optimality Conditions垂Polyhedral Two-Stage Problems422.2.1General Properties422.2.2Expected recourse CostOptimality conditions2.3 General Two-Stage Problems82.3.1Problem Formulation, Interchangeability482.3.2Convex Two-Stage Problems2.4 Nonanticipativity2009/8/20page villContents2.4.1Scenario formulation2.4.2Dualization of Nonanticipativity Constraints2.4.3Nonanticipativity duality for general Distributions2.4.4Value of perfect infExercises3 Multistage problems3. 1 Problem Formulation633.1.1The general setting3.1The Linear case653.1.3Scenario trees3.1.4Algebraic Formulation of nonanticipativity constraints 7lDuality....763.2.1Convex multistage problems·763.2.2Optimality Conditions3.2.3Dualization of Feasibility Constraints3.2.4Dualization of nonanticipativity ConstraintsExercises4 Optimization models with Probabilistic Constraints874.1 Introduction874.2 Convexity in Probabilistic Optimization4.2Generalized Concavity of Functions and measures4.2.2Convexity of probabilistically constrained sets1064.2.3Connectedness of Probabilistically Constrained Sets... 113Separable probabilistic Constraints.1144.3Continuity and Differentiability Properties ofDistribution functions4.3.2p-Efficient Points.1154.3.3Optimality Conditions and Duality Theory1224 Optimization Problems with Nonseparable Probabilistic Constraints.. 1324.4Differentiability of Probability Functions and OptimalityConditions13344.2Approximations of Nonseparable ProbabilisticConstraints134.5 Semi-infinite Probabilistic Problems144E1505 Statistical Inference155Statistical Properties of Sample Average Approximation Estimators.. 1555.1.1Consistency of SAA estimators1575.1.2Asymptotics of the saa Optimal value1635.1.3Second order asStochastic Programs5.2 Stoch1745.2.1Consistency of solutions of the SAA GeneralizedEquatio1752009/8/20pContents5.2.2Atotics of saa generalized equations estimators 1775.3 Monte Carlo Sampling Methods180Exponential Rates of Convergence and Sample sizeEstimates in the Case of a finite Feasible se1815.3.2Sample size estimates in the General Case1855.3.3Finite Exponential Convergence1915.4 Quasi-Monte Carlo Methods1935.Variance-Reduction Techniques198Latin hmpling1985.5.2Linear Control random variables method200ng and likelihood ratio methods 205.6 Validation analysis5.6.1Estimation of the optimality g2025.6.2Statistical Testing of Optimality Conditions2075.7Constrained Probler5.7.1Monte Carlo Sampling Approach2105.7.2Validation of an Optimal solution5.8 SAA Method Applied to Multistage Stochastic Programmin205.8.1Statistical Properties of Multistage SAA Estimators22l5.8.2Complexity estimates of Multistage Programs2265.9 Stochastic Approximation Method2305.9Classical Approach5.9.2Robust sA approach..23359.3Mirror Descent sa method235.9.4Accuracy Certificates for Mirror Descent Sa Solutions.. 244Exercis6 Risk Averse Optimi2536.1 Introductio6.2 Mean-Risk models.2546.2.1Main ideas of mean -Risk analysis546.2.2Semideviation6.2.3Weighted Mean Deviations from Quantiles.2566.2.4Average value-at-Risk2576.3 Coherent risk measures2616.3.1Differentiability Properties of Risk Measures2656.3.2Examples of risk Measures..2696.3.3Law invariant risk measures and Stochastic orders2796.3.4Relation to Ambiguous Chance Constraints2856.4 Optimization of risk measures.2886.4.1Dualization of Nonanticipativity Constraints2916.4.2Examples...2956.5 Statistical Properties of Risk measures6.5.IAverage value-at-Ris6.52Absolute semideviation risk measure301Von mises statistical functionals3046.6The problem of moments306中2009/8/20page xContents6.7 Multistage Risk Averse Optimization3086.7.1Scenario tree formulation3086.7.2Conditional risk mappings3156.7.3Risk Averse multistage Stochastic Programming318Exercises3287 Background material3337.1 Optimization and Convex Analysis..334Directional Differentiability3347.1.2Elements of Convex Analysis3367.1.3Optimization and duality3397.1.4Optimality Conditions.............3467.1.5Perturbation analysis3517.1.6Epiconvergence3572 Probability3597.2.1Probability spaces and random variables7.2.2Conditional Probability and Conditional Expectation... 36372.3Measurable multifunctions and random functions3657.2.4Expectation Functions.3687.2.5Uniform Laws of Large Numbers...,,3747.2.6Law of Large Numbers for Random Sets andSubdifferentials3797.2.7Delta method7.2.8Exponential Bounds of the Large Deviations Theory3877.2.9Uniform Exponential Bounds7.3 Elements of Functional analysis3997.3Conjugate duality and differentiability.......... 4017.3.2Lattice structure4034058 Bibliographical remarks407Biibliography415Index4312009/8/20pageList of Notationsequal by definition, 333IR", n-dimensional space, 333A, transpose of matrix(vector)A, 3336I, domain of the conjugate of risk mea-C(X) space of continuous functions, 165sure p, 262CK, polar of cone C, 337Cn, the space of nonempty compact sub-C(v,R"), space of continuously differ-sets of r 379entiable mappings,176set of probability density functions,I Fr influence function. 3042L, orthogonal of (linear) space L, 41Sz, set of contact points, 3990(1), generic constant, 188b(k; a, N), cdf of binomial distribution,Op(), term, 382214S, the set of &-optimal solutions of theo, distance generating function, 236true problem, 18g(x), right-hand-side derivative, 297Va(a), Lebesgue measure of set A C RdCl(A), topological closure of set A, 334195conv(C), convex hull of set C, 337W,(U), space of Lipschitz continuousCorr(X, Y), correlation of X and Y 200functions. 166. 353CoV(X, Y, covariance of X and y, 180[a]+=max{a,0},2ga, weighted mean deviation, 256IA(, indicator function of set A, 334Sc(, support function of set C, 337n(n.f. p). space. 399A(x), set ofdist(x, A), distance from point x to set Ae multipliers vectors334348dom f, domain of function f, 333N(μ,∑), nonmal distribution,16Nc, normal cone to set C, 337dom 9, domain of multifunction 9, 365IR, set of extended real numbers. 333o(z), cdf of standard normal distribution,epif, epigraph of function f, 333IIx, metric projection onto set X, 231epiconvergence, 377convergence in distribution, 163SN, the set of optimal solutions of the0(x,h)d order tangent set 348SAA problem. 156AVOR. Average value-at-Risk. 258Sa, the set of 8-optimal solutions of thef, set of probability measures, 306SAA problem. 181ID(A, B), deviation of set A from set Bn,N, optimal value of the Saa problem,334156IDIZ], dispersion measure of random vari-N(x), sample average function, 155able 7. 2541A(, characteristic function of set A, 334吧, expectation,361int(C), interior of set C, 336TH(A, B), Hausdorff distance between setsLa」, integer part of a∈R,219A and B. 334Isc f, lower semicontinuous hull of funcN, set of positive integers, 359tion f, 3332009/8/20pageList of notationsRc, radial cone to set C, 337C, tangent cone to set C, 337V-f(r), Hessian matrix of second orderpartial derivatives, 179a. subdifferential. 338a, Clarke generalized gradient, 336as, epsilon subdifferential, 380pos w, positive hull of matrix W, 29Pr(A), probability of event A, 360ri relative interior. 337upper semideviation, 255Le, lower semideviation, 255@R. Value-at-Risk. 25Var[X], variance of X, 149, optimal value of the true problem, 1565=(51,……,5), history of the process,{a,b},186r, conjugate of function/, 338f(x, d), generalized directional deriva-g(x, h), directional derivative, 334O,(, term, 382p-efficient point, 116lid, independently identically distributed,1562009/8/20page xlllPrefaceThe main topic of this book is optimization problems involving uncertain parametersfor which stochastic models are available. Although many ways have been proposed tomodel uncertain quantities stochastic models have proved their flexibility and usefulnessin diverse areas of science. This is mainly due to solid mathematical foundations andtheoretical richness of the theory of probabilitystochastic processes, and to soundstatistical techniques of using real dataOptimization problems involving stochastic models occur in almost all areas of scienceand engineering, from telecommunication and medicine to finance This stimulates interestin rigorous ways of formulating, analyzing, and solving such problems. Due to the presenceof random parameters in the model, the theory combines concepts of the optimization theory,the theory of probability and statistics, and functional analysis. Moreover, in recent years thetheory and methods of stochastic programming have undergone major advances. all thesefactors motivated us to present in an accessible and rigorous form contemporary models andideas of stochastic programming. We hope that the book will encourage other researchersto apply stochastic programming models and to undertake further studies of this fascinatinand rapidly developing areaWe do not try to provide a comprehensive presentation of all aspects of stochasticprogramming, but we rather concentrate on theoretical foundations and recent advances inselected areas. The book is organized into seven chapters The first chapter addresses modeling issues. The basic concepts, such as recourse actions, chance(probabilistic)constraintsand the nonanticipativity principle, are introduced in the context of specific models. Thediscussion is aimed at providing motivation for the theoretical developments in the book,rather than practical recommendationsChapters 2 and 3 present detailed development of the theory of two-stage and multistage stochastic programming problems. We analyze properties of the models and developoptimality conditions and duality theory in a rather general setting. Our analysis coversgeneral distributions of uncertain parameters and provides special results for discrete distributions, which are relevant for numerical methods. Due to specific properties of two- andmultistage stochastic programming problems, we were able to derive many of these resultswithout resorting to methods of functional analvsisThe basic assumption in the modeling and technical developments is that the proba-bility distribution of the random data is not influenced by our actions(decisions). In someapplications, this assumption could be unjustified. However, dependence of probability dis-tribution on decisions typically destroys the convex structure of the optimization problemsconsidered, and our analysis exploits convexity in a significant way
- 2020-12-09下载
- 积分:1