模具設(shè)計外文翻譯----仿真建模與分析

1、<p><b>　　附錄A 英文資料</b></p><p>　　Simulation Modeling and Analysis</p><p>　　The nature of simulation</p><p>　　This is a paper about techniques for using computers to i

2、mitate, or simulate, the operations of various kinds of real-world facilities or processes. The facility or process of interest is usually called a system, and in order to study it scientifically we often have to make a

3、set of assumptions about how it works. These assumptions, which usually take the form of mathematical or logical relationships, constitute a model that is used to try to gain some understanding of how the corresponding s

4、yst</p><p>　　If the relationships that compose the model are simple enough, it may be possible to use mathematical methods (such as algebra, calculus, or probability theory) to obtain exact information on qu

5、estions of interest; this is called an analytic solution. However, most real-world systems are too complex to allow realistic models to be evaluated analytically, and these models must be studied by means of simulation.

6、In a simulation we use a computer to evaluate a model numerically, and data are gather</p><p>　　As an example of the use of simulation, consider a manufacturing company that is contemplating building a large

7、 extension onto one of its plants but is not sure if the potential gain in productivity would justify the construction cost. It certainly would not be cost-effective to build the extension and then remove it later if it

8、does not work out. However, a careful simulation study could shed some light on the question by simulating the operation of the plant as it currently exists and as it wo</p><p>　　Application areas for simula

9、tion are numerous and diverse. Below is a list of some pay of problems for which simulation has been found to be a useful and powerful tool:</p><p>　　Designing and analyzing manufacturing systems</p>

10、<p>　　Evaluating military weapons systems or their logistics requirements</p><p>　　Determining hardware requirements or protocols for communications networks</p><p>　　Determining hardware a

11、nd software requirements for a computer system</p><p>　　Designing and operating transportation systems such as airports,freeways,ports,and subways</p><p>　　Evaluating designs for service organiz

12、ations such as call centers, fast-food restaurants hospitals, and post offices</p><p>　　Reengineering of business processes</p><p>　　Determining ordering policies for an inventory</p><

13、;p>　　Analyzing financial or economic systems</p><p>　　Simulation is one of the most widely used operations research and management-science techniques, if not the most widely used. One indication of this

14、is the Winter Simulation Conference, which attracts 600 to 700 people every year. In addition, there are several simulation vendor users' conferences with more than 100 participants per year.</p><p>　　Th

15、ere are also several surveys related to the use of operations research techniques. For example, Lane, Mansour, and Harpell ( 1993 ) reported from a longitudinal study, spanning 1973 through 1988, that simulation was cons

16、istently ranked as one of the three most important "operations- research techniques." The other two were "math programming" (a catch-all term that includes many individual techniques such as linear pr

17、ogramming, nonlinear programming, etc.) and "statistics" (which is not an oper</p><p>　　There have been, however, several impediments to even wider acceptance and usefulness of simulation. First, m

18、odels used to study large-scale systems tend to be very complex, and writing computer programs to execute them can be an arduous task indeed. This task has been made much easier in recent years by the development of exce

19、llent software products that automatically provide many of the features needed to "program" a simulation model. A second problem with simulation of complex systems is that</p><p>　　Systems, models,

20、 and simulation</p><p>　　A system is defined to be a collection of entities, e.g. people or machines that act and interact together toward the accomplishment of some logical end. In practice, what is meant b

21、y "the system" depends on the objectives of a particular study. The collection of entities that comprise a system for one study might be only a subset of the overall system for another. For example, if one want

22、s to study a bank to determine the number of tellers needed to provide adequate service for customers who wa</p><p>　　We categorize systems to be of two types, discrete and continuous. A discrete system is o

23、ne for which the state variables change instantaneously at separated points in time. A bank is an example of a discrete system, since state variables -- e. g. the number of customers in the bank – change only when a cust

24、omer arrives or when a customer finishes being served and departs. A continuous system is one for which the state variables change continuously with respect to time. An airplane moving throug</p><p>　　At som

25、e points in the lives of most systems, there is a need to study them to try to gain some insight into the relationships among various components, or to predict performance under some new conditions.</p><p>　

26、　Figure 1. Ways to study a system</p><p>　　Experiment with the Actual System vs. Experiment with a Model of the System. If it is possible ( and cost-effective) to alter the system physically and then let it

27、 operate under the new conditions, it is probably desirable to do so, for in this case there is no question about whether what we study is valid. However, it is rarely feasible to do this, because such an experiment woul

28、d often be too costly or too disruptive to the system. For example, a bank may be contemplating reducing the number </p><p>　　Physical Model vs. Mathematical Model. To most people, the word "model"

29、 evokes images of clay cars in wind tunnels, cockpits disconnected from their air planes to be used in pilot training, or miniature supertankers scurrying about in a swimming pool. These are examples of physical models (

30、also called iconic models), and are not typical of the kinds of models that are usually of interest in operations research and systems analysis. Occasionally, however, it has been found useful to build physica</p>

31、<p>　　Analytical Solution vs. Simulation. Once we have built a mathematical model, it must then be examined to see how it can be used to answer the questions of interest about the system it is supposed to represent

32、. If the model is simple enough, it may be possible to work with its relationships and quantities to get an exact, analytical solution. In the d = rt example, if we know the distance to be traveled and the velocity, then

33、 we can work with the model to get t = d/r as the time that will be requ</p><p>　　While there may be a small dement of troth to pejorative old saws such as "method of last resort" sometimes used to

34、 describe simulation, the fact is that we are very quickly led to simulation in most situations, due to the sheer complexity of the systems of interest and of the models necessary to represent them in a valid way.</p&

35、gt;<p>　　Given, then, that we have a mathematical model to be studied by means of simulation (henceforth referred to as a simulation model), we must then look for particular tools to do this. It is useful for this

36、 purpose to classify simulation models along three different dimensions:</p><p>　　Static vs. Dynamic Simulation Models. A static simulation model is a representation of a system at a particular time, or one

37、that may be used to represent a system in which time simply plays no role; examples of static simulations are Monte Carlo models. On the other hand, a dynamic simulation model represents a system as it evolves over time,

38、 such as a conveyor system in a factory.</p><p>　　Deterministic vs. Stochastic Simulation Models. If a simulation model does not contain any probabilistic (i. e. random) components, it is called deterministi

39、c; a complicated (and analytically intractable) system of differential equations describing a chemical reaction might be such a model. In deterministic models, the output is "determined" once the set of input q

40、uantities and relationships in the model have been specified; even though it might take a lot of computer time to evaluate what it is</p><p>　　Continuous vs. Discrete Simulation Models. Loosely speaking, we

41、define discrete and continuous simulation models analogously to the way discrete and continuous systems were defined above. It should be mentioned that a discrete model is not always used to model a discrete system, and

42、vice versa. The decision whether to use a discrete or a continuous model for a particular system depends on the specific objectives of the study. For example, a model of traffic flow on a freeway would be discrete if<

43、/p><p>　　Advantages, disadvantages, and pitfalls of simulation</p><p>　　We conclude by listing some good and bad characteristics of simulation (as opposed to other methods of studying systems), and

44、 by noting some common mistakes made in simulation studies that can impair or even ruin a simulation project. Simulation is a widely used and increasingly popular method for studying complex systems. Some possible advant

45、ages of simulation that may account for its widespread appeal are the following:</p><p>　　Most complex, real-world systems with stochastic elements cannot be accurately described by a mathematical model that

46、 can be evaluated analytically. Thus, a simulation is often the only type of investigation possible.</p><p>　　Simulation allows one to estimate the performance of an existing system under some projected set

47、of operating conditions. </p><p>　　Alternative proposed system designs (or alternative operating policies for s single system) can be compared via simulation to see which best meets a specified requirement.&

48、lt;/p><p>　　In a simulation we can maintain much better control over experimental conditions than would generally be possible when experimenting with the system itself.</p><p>　　Simulation allows u

49、s to study a system with a long time frame -- e.g. an economic system--in compressed time, or alternatively to study the detailed workings of a system in expanded time.</p><p>　　Simulation is not without its

50、 drawbacks. Some disadvantages are as follows:</p><p>　　Each run of a stochastic simulation model produces only estimates of a model's true characteristics for a particular set of input parameters. Thus,

51、 several independent runs of the model will probably be required for each set of input parameters to be studied. For this reason, simulation models are generally not as good at optimization as they are at comparing a fix

52、ed number of specified alternative system designs. On the other hand, an analytic model, if appropriate, can often easily produce the</p><p>　　Simulation models are often expensive and time-consuming to deve

53、lop.</p><p>　　The large volume of numbers produced by a simulation study or the persuasive impact of a realistic animation often creates a tendency to place greater confidence in a study's results than i

54、s justified. If a model is not a "valid" representation of a system under study, the simulation results, no matter how impressive they appear, will provide little useful information about the actual system.<

55、/p><p>　　When deciding whether or not a simulation study is appropriate in a given situation, we can only advise that these advantages and drawbacks be kept in mind and that all other relevant facets of one'

56、;s particular situation be brought to bear as well. Finally, note that in some studies both simulation and analytic models might be useful. In particular, simulation can be used to check the validity of assumptions neede

57、d in an analytic model. On the other hand, an analytic model can suggest reasonable</p><p>　　Assuming that a decision has been made to use simulation, we have found the following pitfalls to the successful c

58、ompletion of simulation study:</p><p>　　Failure to have a well-defined set of objectives at beginning of the simulation study;</p><p>　　Inappropriate level of model detail;</p><p>　

59、　Failure to communicate with management throughout the course of the simulation study;</p><p>　　Misunderstanding of simulation by management;</p><p>　　Treating a simulation study as if it were p

60、rimarily an exercise in computer programming;</p><p>　　Failure to have people with a knowledge of simulation methodology and statistics on the modeling team;</p><p>　　Failure to collect good sys

61、tem data;</p><p>　　Inappropriate simulation software;</p><p>　　Obliviously using simulation software products whose complex macro statements may not be well documented and may not implement the

62、desired modeling logic</p><p>　　Belief that easy-to-use simulation packages, which require little or no programming, require a significantly lower level of technical competence;</p><p>　　Misuse

63、of animation;</p><p>　　Failure to account correctly for sources of randomness in the actual system;</p><p>　　Using arbitrary distributions (e. g. normal, uniform, or triangular) as input to the

64、simulation.</p><p>　　Analyzing the output data from one simulation run(replication)using formulas that assume independence;</p><p>　　Making a single replication of a particular system design and

65、 treating the output statistics as the “true answers”;</p><p>　　Comparing alternative system designs on the basis of one replication for each design;</p><p>　　Using the wrong performance measure

66、s.</p><p><b>　　附錄B 中文翻譯</b></p><p><b>　　仿真建模與分析</b></p><p>　　這是一篇關(guān)于使用計算機(jī)來進(jìn)行模擬真實設(shè)施操作技術(shù)的文章。這種設(shè)施和操作方法通常被稱作為系統(tǒng)。為了有效的學(xué)習(xí)和研究它。我們經(jīng)常需要做出一系列的關(guān)于它是怎么工作的假定。這些以數(shù)學(xué)方式或者以邏輯方式

67、存在的假定構(gòu)成了一個模型。經(jīng)常，通過這個模型我們能獲得一些關(guān)于這個系統(tǒng)對一些行為的反應(yīng)的理解。</p><p>　　如果組成模型的聯(lián)系夠簡單的,那么就有可能使用數(shù)學(xué)上的方法 (像是代數(shù)學(xué)，微積分［學(xué)］或率理論) 獲得關(guān)于重要問題的精確信息; 這被稱作為分析的方法。 然而，大多數(shù)的真實系統(tǒng)太復(fù)雜而被現(xiàn)實的模型來進(jìn)行分析和評估，在一個模擬中我們經(jīng)常使用一部計算機(jī)數(shù)字地評估一個模型，而且收集數(shù)據(jù)是為了要估計那些需要的模

68、型的真實特性。</p><p>　　當(dāng)作模擬使用的一個例子, 假想一個制造公司，正在打算在它的藍(lán)圖之上建造一個大的擴(kuò)建，但是不確定在生產(chǎn)中的潛力能不能達(dá)到贏利的目的。、當(dāng)然了，在當(dāng)知道它達(dá)不到預(yù)先的利潤的時候再去消除這種擴(kuò)建并不是一種明智的做法 然而，在工廠進(jìn)行擴(kuò)建之前，一項小心的藉由模擬工廠的模擬操作會事先弄明白問題所在。</p><p>　　使用模擬的領(lǐng)域是不同的，多種多樣的。 在下面

69、的是一連串的一些模擬已經(jīng)被發(fā)現(xiàn)是一個強(qiáng)有力的工具的領(lǐng)域:</p><p>　　設(shè)計和分析制造業(yè)的系統(tǒng)</p><p>　　評估軍事武器或他們的物流管理需求的系統(tǒng)</p><p>　　為通信網(wǎng)絡(luò)決定五金需求或協(xié)定的系統(tǒng)</p><p>　　為一個計算機(jī)系統(tǒng)決定五金和軟件需求的系統(tǒng)</p><p>　　設(shè)計和操作運輸系統(tǒng)

70、, 像是航空站，快速車道，港埠和地道</p><p>　　為服務(wù)組織而設(shè)計的系統(tǒng) , 像是呼叫評估設(shè)計置中醫(yī)院和郵局的速食食堂</p><p>　　為事業(yè)的再發(fā)展工程而設(shè)計的系統(tǒng)</p><p>　　為程序化決策而設(shè)計的系統(tǒng)</p><p>　　分析財政的或經(jīng)濟(jì)的系統(tǒng)</p><p>　　模擬在操作研究和管理科學(xué)中應(yīng)用

71、最為流行的一種技術(shù)。 如果不是最廣泛地使用過者。一個事實就能說明問題，在冬天舉行的一個模擬會議,每個模擬都能吸引 600 到 700 個人。除此之外,有一些模擬投資商的會議每年的參與者超過 100個。</p><p>　　同時，還有一些關(guān)于操作研究技術(shù)的調(diào)查。 舉例來說，Lane， Mansour 和 Harpell(1993) 根據(jù)一項長期的研究寫報告,時間跨越 1973 直到 1988, 那個模擬一致地被認(rèn)為

72、是三個最重要的 "操作- 研究技術(shù)中的之一。" 另外二是 " 數(shù)學(xué)作預(yù)定表 " 和 " 統(tǒng)計表".( 就本身而言不是一個操作- 研究的技巧)。 Gupta(1997) 分析了從1970 到 1992來自橋頸介面 (正在處理操作研究的應(yīng)用程序的領(lǐng)導(dǎo)橋頸之一) 的 1294個文件, 發(fā)現(xiàn)那一個模擬是只有對在 13個被考慮的技巧之中的 " 數(shù)學(xué)作預(yù)定表 " 。&l

73、t;/p><p>　　然而,已經(jīng)有一些甚至比模擬更為有用，也更為廣泛的為大家所接受的措施和方法。首先，模型常常用來學(xué)習(xí)和研究大規(guī)模的非常復(fù)雜的系統(tǒng), 而且寫電腦程序來運行他們也可能的確是一個費力的任務(wù)。在近些年來，由于卓越軟件的使用 這一個任務(wù)的實現(xiàn)已經(jīng)變的容易多了。這些軟件可以自動的給一個模擬模型提供所需要的特征。第二個對于大的復(fù)雜的系統(tǒng)的問題是：有些時候計算機(jī)需要耗費很多的時間。但是現(xiàn)在這個問題已經(jīng)變的不是那么嚴(yán)

74、重。因為計算機(jī)的速度越來越快，計算機(jī)也越來約便宜。 結(jié)果，一些模擬研究對于許多項目來說已經(jīng)變成得到最終答案的唯一途徑。 我們擔(dān)心一種態(tài)度：這種態(tài)度忽略了對于一個系統(tǒng)來說怎么樣做才算合適，才能發(fā)揮系統(tǒng)的最大優(yōu)勢。毫無 疑問這種態(tài)度回導(dǎo)致這樣一種結(jié)果。那就是從模擬研究中得到許多錯誤的結(jié)論。</p><p><b>　　系統(tǒng)，模型和模擬</b></p><p>　　一個系

75、統(tǒng)被定義是一個實質(zhì)的集合物件, 舉例來說人或機(jī)器，每一個行為而且向著一些合乎邏輯的端成就一起互動。 在練習(xí)中，一個系統(tǒng)是的含義是什么取決于特殊的研究。 對于一個研究來說，一些實體的集合構(gòu)成的一個系統(tǒng)也許是另外一個系統(tǒng)的子集。 舉例來說，如果一個人想通過學(xué)習(xí)銀行的管理和政策來決定被需要信息，應(yīng)該向一些僅僅需要向銀行存儲現(xiàn)金的人或者提取現(xiàn)金的顧客提供適當(dāng)?shù)姆?wù),系統(tǒng)能被定義為銀行中職員和顧客之間的那部分。 另一方面，如果負(fù)責(zé)貸款的職員和安

76、全把錢放入保險柜這部分包括在內(nèi),明顯的，系統(tǒng)的定義一定要被擴(kuò)大。 相對于研究的實體， 我們定義系統(tǒng)的狀態(tài)是在一個特別的時間描述一個系統(tǒng)所需的變量的那一個實體的組合。在銀行的一項研究中，可能的狀態(tài)變量是忙著講話的銀行職員,顧客的數(shù)量 , 和每個顧客到達(dá)銀行的時間。</p><p>　　我們把系統(tǒng)分為二個類型，離散［的］和連續(xù)。 安培離散［的］系統(tǒng)是狀況變量就是在時間段上變化不連續(xù)的量。, 從狀態(tài)和變量來說 ， 銀行

77、就是離散系統(tǒng)一個例子。 只有當(dāng)一個顧客到達(dá)的時候或當(dāng)一個顧客被服務(wù)完而且離開的時候，銀行的顧客數(shù)目才會變化。 一個連續(xù)式的系統(tǒng)就是狀態(tài)變量是連續(xù)的。例如， 一個在空中移動過的飛機(jī)是連續(xù)式的, 因為狀況變數(shù)量, 象位移和速度在時間上能連續(xù)地變化。對于 練習(xí)的少數(shù)系統(tǒng)來說，可能整個系統(tǒng)是不連續(xù)或整個是連續(xù); 但是對于大多數(shù)系統(tǒng)來,它自身的類型的變化是取決與自己的。 我們通常把一個系統(tǒng)歸類為連續(xù)的或者是不連續(xù)的。</p><

78、;p>　　在大多數(shù)系統(tǒng)的生命測點中，,有一種需要，這種需要是學(xué)習(xí)他們，從而得到在各種不同的結(jié)構(gòu)之中的相對聯(lián)系, 或在一些新的條件之下的預(yù)測性能。</p><p>　　把一個實際系統(tǒng)的實驗和模擬系統(tǒng)的實驗做比較。 如果可以從物理上改變系統(tǒng)，然后在新的條件式之下對它進(jìn)行操作是可能的話( 和有成本效益的),這么做可能是實際需要的, 因為在這情況我們所研究的領(lǐng)域不出現(xiàn)問題是有效的。 然而, 這種可能是不大, 因為象

79、這樣一個系統(tǒng)的成本是很貴的。 舉例來說，一個銀行可能打算減少職員的數(shù)目減少成本，但是實際上這種嘗試這可能導(dǎo)致顧客的延遲和疏遠(yuǎn)。 更圖形象的說，這種系統(tǒng) " 系統(tǒng) " 甚至不可能存在，但是我們想要在它的各種不同的狀態(tài)下研究學(xué)習(xí)它，看看怎樣來優(yōu)化建造它的平面。通信網(wǎng)絡(luò) 可能是這種情形的一個例子 , 或者一個策略的核武器系統(tǒng)也是這種情形的一個例子。 因為這些原因,通常建造一個模型來代表實際系統(tǒng)是必要的，通過研究學(xué)習(xí)他它來優(yōu)

80、化實際的系統(tǒng)。 當(dāng)使用一個模型 的時候, 總是存在這樣一個問題：模型是否準(zhǔn)確的反映了我們?yōu)榱四承┠康亩O(shè)計的系統(tǒng)。</p><p>　　實際的模型和數(shù)學(xué)模型比較。 對大多數(shù)的人，模型那個字會喚起風(fēng)道的黏土車的鏡像,駕駛間在他們的飛機(jī)分離中被用于引示培訓(xùn), 或小型超大型油輪一個游泳池中匆忙地跑。 這些是實際模型 ( 也叫做實體套式) 的例子, 并不是通常是對行動研究和系統(tǒng)分析的興趣模型的典型類型。 然而，有時候，我

81、們已經(jīng)被發(fā)現(xiàn)用建立實際的模型學(xué)習(xí)工程或管理系統(tǒng)是很有用的; 但是相當(dāng)多的為如此的目的而建造的模型是數(shù)學(xué)上的, 代表一個系統(tǒng)會根據(jù)合乎邏輯而被操縱和系統(tǒng)如何反應(yīng)之間的數(shù)量聯(lián)系, -如果數(shù)學(xué)套式是一個有效的，那么就會見到系統(tǒng)是如何反應(yīng)的。也許一個數(shù)學(xué)套式的模型是最簡單熟悉的 關(guān)系 d=rt，r是行速率。,t是移動的時間，而且 d 是被移動的距離。 在一個事例中，這可能提供一個有效的模型 ( 舉例來說對另外的一個行星的一個太空探測在它已經(jīng)達(dá)到

82、它的速度班機(jī)之后 )， 但是對于其他的目的這是一個很沒有力度的模型。 (e.g.rush- 在擁擠的都市上的小時交換快速車道)</p><p>　　分析的方法和模擬比較。我們已經(jīng)建造一個數(shù)學(xué)模型, 它一定會被檢測用來觀察它所代表的系統(tǒng)對于一個問題是怎么反應(yīng)的 如果這個模型足夠簡單,與它的聯(lián)系和數(shù)量結(jié)合會得到一個正確的分析方法是有可能的。 在 d=rt 的例子 中，如果我們知道要移動的距離和速度,然后我們能與模型

83、聯(lián)系就會得到被需要的時間 t= d/r 。 這個非常簡單，但是一些分析的方法特別復(fù)雜,需要巨大的計算資源; 開發(fā)一個大的非稀疏的收放熱件是一個眾所周知例子，但是在一個給定的例子中數(shù)字地獲得它是很很遠(yuǎn)的事情。 如果對一個數(shù)學(xué)的分析解決辦法是可得的并且使用計算機(jī)是很有效的, 那么它通常是人們想要的途徑來進(jìn)行學(xué)習(xí)研究，而不是緊緊進(jìn)行一個模擬。 然而，許多系統(tǒng)高度復(fù)雜,所以想要獲得他們的有效數(shù)學(xué)模型是很難的,預(yù)先排除任何一個分析方法的可能性。

84、在這情況，模型一定用模擬來被研究, 對輸入的模型進(jìn)行數(shù)字研究來觀測它們是如何影響輸出的。</p><p>　　然后, 我們通過模擬的方法來學(xué)習(xí)一個數(shù)學(xué)的( 自此以后稱為一個模擬套式) 模型,我們一定找到特別的工具做這些。 出于這樣的目的而把模擬模型同其它三種不同的類型進(jìn)行歸類是非常有用的:</p><p>　　靜態(tài)和動態(tài)的模型。 在特定的時候，一個靜態(tài)的模擬模型是一個系統(tǒng)的代表, 或者在系

85、統(tǒng)扮演一個簡單的角色的時候可以用做代表; 靜態(tài)模擬的例子是蒙地卡羅模型。另一方面，當(dāng)隨著時間的變動的時候，一個動態(tài)的模型可以代表一個系統(tǒng),像是在一個工廠的一個輸送機(jī)系統(tǒng)。</p><p>　　定性的對隨機(jī)的模擬模型。 如果一個模擬模型不含有任何偶然性的 (i。 e。 亂砌)組件, 它被叫做定性模型; 不同的描述化學(xué)反應(yīng)的復(fù)雜 ( 和分析不聽話) 系統(tǒng)可能是一個如此的模型。 在定性的模型中，一旦模型一系列的輸入關(guān)系

86、被定義后輸出是起堅決作用的，即使它可能會花費計算機(jī)的許多時間來進(jìn)行自身的調(diào)整。然而，許多系統(tǒng)的模型至少有一個自由輸入變量，而且這些會關(guān)聯(lián)到其它的隨機(jī)模型。 大部分排隊和物品系統(tǒng)被稱作隨機(jī)模擬模型。 隨機(jī)模擬模型輸出它本身的自由變量，因此必須被當(dāng)作評估模型特點的一個重要依據(jù); 這是模擬的主要缺點之一。</p><p>　　連續(xù)和不連續(xù)的模擬比較。 不嚴(yán)格的說,我們以類似的方式定義離散［的］和連續(xù)模擬模型。這些必須

87、涉及這里的離散的模型并不是我們過去經(jīng)常提到的模型。 一個關(guān)于是否使用一個離散［的］或一個連續(xù)的模型作為一個特別的系統(tǒng)的決斷依賴于研究的特定對象。舉例來說，在一個快速車道上，如果每個車輛個體的特點是很重要的，那么的一個交通流量的模型就會是離散［的］。</p><p><b>　　模擬的優(yōu)點，缺點。</b></p><p>　　我們藉由列出一些模擬 ( 如其他方法所反對學(xué)

88、習(xí)系統(tǒng)) 的壞的好的特性來得出總結(jié)，而且藉由注意在模擬中通常被犯的一些可以損壞整個系統(tǒng)的錯誤也可以得出結(jié)論。 模擬是學(xué)習(xí)復(fù)雜的系統(tǒng)一個廣泛使用過和逐漸常用的方法。 下面的一些是可以代表它廣泛流行的原因:</p><p>　　大多數(shù)復(fù)雜的帶隨機(jī)性的真實性系統(tǒng)不能夠正確地被一個可以分析數(shù)學(xué)的模型來描述。 因此，一個模擬時常不是唯一類型的結(jié)果可能的。</p><p>　　模擬允許被用來評估一個真

89、實存在的系統(tǒng)在被操作狀態(tài)下的外型</p><p>　　替換一個系統(tǒng)的組成來進(jìn)行比較回找到最好的符合需求的模擬 ( 或替換物操作政策對于 s 獨身者系統(tǒng)) 在一個模擬中我們通常能在實驗的狀態(tài)下維持的更好。</p><p>　　模擬允許我們用一個長的時框?qū)W習(xí)一個系統(tǒng) -- 舉例來說一個經(jīng)濟(jì)的系統(tǒng)--在被有限的時間中, 或二者擇一地在長時間中學(xué)習(xí)系統(tǒng)的詳細(xì)操作。</p><p

90、>　　模擬不是沒有它的不利點。 一些缺點如下列各項:</p><p>　　每個一個隨機(jī)程序模擬模型的運行只有在一些反應(yīng)它自身真實特點的參數(shù)輸入的情況下才能進(jìn)行。 因此，一些模型的獨立運行很有可能要求輸入你所研究學(xué)習(xí)的一系列變量。 </p><p>　　模擬模型時常是貴的和需要耗費時間來進(jìn)行發(fā)展。</p><p>　　當(dāng)決定的是否在一個給定的局勢中進(jìn)行一項模擬研

91、究的時候, 我們才能想到保存在腦海中那些有利的不利的方面。 最后, 注意那些在研究模擬和分析的模型可能都是有用的系統(tǒng)。 尤其，模擬能用來檢查在一個分析的模型中被需要的假設(shè)準(zhǔn)確性。 另一方面，一個分析的模型能給出一個合理的建議供調(diào)查學(xué)習(xí)。</p><p>　　假定哪一個決斷已經(jīng)被作出使用模擬,我們已經(jīng)發(fā)現(xiàn)對模擬的成功完備化研究中有下列陷阱:</p><p>　　在一個模擬研究的開始就失敗在定

92、義明確的對象上;</p><p><b>　　不適當(dāng)模型細(xì)節(jié);</b></p><p>　　在一個模擬研究的溝通和管理的過程中被破壞。</p><p><b>　　模擬的管理誤會;</b></p><p>　　處理一項模擬研究好像它主要地是在進(jìn)行計算機(jī)方案一種練習(xí);</p><p

93、>　　在一個模型的團(tuán)隊中在失敗在缺乏具有模擬和統(tǒng)計知識的人</p><p>　　失敗在收集系統(tǒng)數(shù)據(jù);</p><p><b>　　不適當(dāng)?shù)哪M軟件;</b></p><p>　　失敗在有這樣的一個信念：模擬包是很容易運用的。實際上它是需要一些技巧的，需要一些有一定技術(shù)水平的</p><p><b>　　動

94、畫的誤用;</b></p><p>　　失敗在正確解釋實際的系統(tǒng)的任意源點;</p><p>　　使用不定的分配 (e.g. 法線，均勻, 或三角形的 )作為系統(tǒng)的輸入。</p><p>　　分析來自正在運用公式運行的系統(tǒng)的輸出數(shù)據(jù)，而這些都是獨立的;</p><p>　　制造特別的系統(tǒng)而設(shè)計的一個單一響應(yīng)而且把輸出的統(tǒng)計數(shù)字當(dāng)作