美赛模拟题2
2015年美国数学建模竞赛第二次模拟赛题
Problem A Warmer Days or Sour Grapes ?The high quality of wines（葡萄酒）produced in the Finger Lakes Region（五指湖区）of upstate （北部）New York is widely known. Proximity（接近）to lakes tempers the climate and makes it more suitable for growing several varieties of premium（独特）grapes: R iesling（雷司令）, G ewürztraminer（琼瑶浆）,C hardonnay（霞多丽）, M erlot（梅洛）, P inot Noir（黑比诺）, and CabernetF ranc（品丽珠）. (There are many more, but we will restrict（限制）the discussion to these six to simplify（简化）the modeling.) Each variety has its own preferred “average temperature” range but is also different in its susceptibility（感受性）to diseases and ability to withstand（抵抗）short periods of unusually cold temperature.As our local climate changes, the relative suitability of these varieties will be changing as well. A forward-looking winery（酒厂）has hired your team to help with the long-term planning. You will need to recommenda) the proportion（比例）of the total vineyard（葡萄园）to be used for growing each of the above six varieties;b) and when should these changes be implemented （实施）(based on observed temperatures and/or current market prices for each type of wine).Naturally, the winery is interested in maximizing its annual profit. But since the latter （后者）is weather-dependent, it might vary a lot year-to-year. You are also asked to evaluate the trade-offs （权衡）between optimizing the expected/average case versus the worst(-realistic-)scenario（情景）.Things to keep in mind:Climate modeling is complicated（复杂）and predicting the rate of “global warming” is a hotly debated area. For the purposes of this problem, assume that the annual average temperature in Ithaca（伊萨卡）, NY will increase by no more than 4°C by the end of this century.It is not all about the average temperature – a short snap（临时）of sub- zero（零度）temperature in late Ferburay or early March (after the vines already started getting used to warmer weather) is far more damaging than the same low temperature would be in the middle of the winter.It takes at least 3 years for a newly planted vine to start producing grapes suitable for winemaking.Problem B Outlook of Car-to-Car TechSAN FRANCISCO -- After more than a decade of research into car-to-car communications, U.S. auto safety regulators took a step forward today by unveiling their plan for requiring cars to have wireless gear that will enable them to warn drivers of danger.These vehicle-to-vehicle (V2V) transmitters and software could save thousands of lives and prevent hundreds of thousands of crashes each year by providing cars with information they never will be able to gather simply from cameras and sensors. “Safety is our top priority, and V2V technology represents the next great advance in saving lives,” Transportation Secretary Anthony Foxx said in an announcement. “This technology could move us from helping people survive crashes to helping them avoid crashes altogether.”Requirement 1: Present a mathematical model to discuss the reduction of the number of traffic accidents and road fatalities/injuries in San Francisco by V2V technology. Requirement 2: Determine the maximum number of cars in San Francisco due to the V2V technology.Requirement 3: Discuss the benefits of V2V technology to alleviate road congestion. Requirement 4: Provide your recommendation to the government.Prblem C Forest FiresOne major environmental concern is the occurrence of forest fires (also called wildfires), which affect forest preservation, bring economical and ecological damage and endanger human lives. Such phenomenon is due to multiple causes (e.g. human negligence and lightnings). Despite an increasing of state expenses to control this disaster, each year millions of forest hectares (ha) are destroyed all around the world.Fast detection is an important element for successful firefighting. Traditional human surveillance is expensive and affected by subjective factors, there has been an emphasis to develop automatic solutions, such as satellite-based, infrared/smoke scanners and local sensors (e.g. meteorological). Propagation models try to describe the future evolution of the forest fire given an initial scenario and certain input parameters. Modeling the dynamical behavior of fire propagation in a forest is helpful for creating scheme to control and fight fire.Requirement 1 Describe several different metrics that could be used to evaluate the effectiveness of fire detection. Could you combine your metrics to make them even more useful for measuring quality?Requirement 2 Model the dynamical behavior of fire spread in a forest. Requirement 3 Discuss the factors to affect fire occurrence. Which factors are the most critical in causing fires. Build mathematical models to predict the burned area of fires using Meteorological Data.Requirement 4 Give y our suggestion for preventing from forest fire and fighting against it.Problem D Wearable Activity RecognitionThe percentage of EU citizens aged 65 years or over is projected to increase from 17.1% in 2008 to 30.0% in 2060. In particular, the number of 65 years old is projected to rise from 84.6 million to 151.5 million, while the number of people aged 80 or over is projected to almost triple from 21.8 million to 61.4 million (EUROSTAT: New European Population projections 2008–2060). It has been calculated that the purely demographic effect of an ageing population will push up health-care spending by between 1% and 2% of the gross domestic product (GDP) of most member states. At first sight this may not appear to be very much when extended over several decades, but on average it would in fact amount to approximately a 25% increase in spending on health care, as a share of GDP, in the next 50 years (European Economy Commission, 2006). The effective incorporation of technology into health-care systems could therefore be decisive in helping to decrease overall public spending on health. One of these emerging health-care systems is daily living physical activity recognition.Daily living physical activity recognition is currently being applied in chronic disease management (Amft & Troter, 2008; Zwartjes, Heida, van Vugt, Geelen, & Veltink, 2010), rehabilitation systems (Sazonov, Fulk, Sazonova, & Schuckers, 2009) and disease prevention (Sazonov, Fulk, Hill, Schutz, & Browning, 2011; Warren et al., 2010), as well as being a personal indicator to health status (Arcelus et al., 2009). One of the principal subjects of the health related applications being mooted is the monitoring of the elderly. For example, falls represent one of the major risks and obstacles to old people’s independence (Najafi, Aminian, Loew, Blanc, & Robert, 2002; Yu, 2008). This risk is increased when some kind of degenerative disease affects them. Most Alzheimer’s patients, for exa mple, spend a long time every day either sitting or lying down since they would otherwise need continuous vigilance and attention to avoid a fall.The registration of daily events, an important task in anticipating and/or detecting anomalous behavior patterns and a primary step towards carrying out proactive management and personalized treatment, is normally poorly accomplished by patients’ families, healthcare units or auxiliary assistants because of limitations in time and resources. Automatic activity-recognition systems could allow us to conduct a completely detailed monitoring and assessment of the individual, thus significantly reducing current human supervision requirements.Most wearable activity recognition systems assume a predefined sensor deployment that remains unchanged during runtime. However, this assumption does not reflect real-life conditions. During the normal use of such systems, users may place the sensors in a position different from the predefined sensor placement. Also, sensors may move from their original location to a different one, due to a loose attachment. Activity recognition systems trained on activity patterns characteristic of a given sensor deployment may likely fail due to sensor displacements.Your task is as follows.(1) Build models to recognize daily living activities.(2) Explore the effects of sensor displacement induced by both the intentionalmisplacement of sensors and self-placement by the user.(3) Verify your recognition models’ toleranc e to sensor displacement.Data Set Information:The REALDISP (REAListic sensor DISPlacement) dataset has been originally collected to investigate the effects of sensor displacement in the activity recognition process in real-world settings. It builds on the concept of ideal-placement, self-placement and induced- displacement. The ideal and mutual-displacement conditions represent extreme displacement variants and thus could represent boundary conditions for recognition algorithms. In contrast, self-placement reflects a users perception of how sensors could be attached, e.g., in a sports or lifestyle application. The dataset includes a wide range of physical activities (warm up, cool down and fitness exercises), sensor modalities (acceleration, rate of turn, magnetic field and quaternions) and participants (17 subjects). Apart from investigating sensor displacement, the dataset lend itself for benchmarking activity recognition techniques in ideal conditions.Dataset summary:#Activities: 33#Sensors: 9#Subjects: 17#Scenarios: 3ACTIVITY SET:A1: WalkingA2: JoggingA3: RunningA4: Jump upA5: Jump front & backA6: Jump sidewaysA7: Jump leg/arms open/closedA8: Jump ropeA9: Trunk twist (arms outstretched)A10: Trunk twist (elbows bent)A11: Waist bends forwardA12: Waist rotationA13: Waist bends (reach foot with opposite hand)A14: Reach heels backwardsA15: Lateral bend (10_ to the left + 10_ to the right)A16: Lateral bend with arm up (10_ to the left + 10_ to the right)A17: Repetitive forward stretchingA18: Upper trunk and lower body opposite twistA19: Lateral elevation of armsA20: Frontal elevation of armsA21: Frontal hand clapsA22: Frontal crossing of armsA23: Shoulders high-amplitude rotationA24: Shoulders low-amplitude rotationA25: Arms inner rotationA26: Knees (alternating) to the breastA27: Heels (alternating) to the backsideA28: Knees bending (crouching)A29: Knees (alternating) bending forwardA30: Rotation on the kneesA31: RowingA32: Elliptical bikeA33: CyclingSENSOR SETUP:Each sensor provides 3D acceleration (accX,accY,accZ), 3D gyro (gyrX,gyrY,gyrZ), 3D magnetic field orientation (magX,magY,magZ) and 4D quaternions (Q1,Q2,Q3,Q4). The sensors are identified according to the body part on which is placed respectively:。
2017年美赛题目
2017年美赛题目2017年美赛（MCM/ICM）共有6个题目，分别是：1. Problem A: The Art of Archery.这个问题涉及到弓箭射击的技术和策略。
要求团队通过建立数学模型，分析射击的精度和效率，并给出最佳的射击策略。
2. Problem B: The Search for the Lost Dutchman's Gold Mine.这个问题要求团队通过模拟和优化算法，规划一次寻找失落的荷兰人金矿的探险。
团队需要考虑资源分配、路线选择和风险评估等因素。
3. Problem C: The Great American Potato Chip Factory.这个问题要求团队分析和优化一个薯片工厂的生产过程。
团队需要考虑原材料采购、生产调度和产品分配等方面，以提高工厂的效率和利润。
4. Problem D: The Mathematics of Music.这个问题要求团队通过数学模型和计算方法，分析和优化音乐的和声和旋律结构。
团队需要考虑音乐的音高、音长和节奏等因素，并给出最佳的音乐创作建议。
5. Problem E: The Internet of Things.这个问题要求团队分析和优化物联网中的传感器网络。
团队需要考虑传感器的部署、数据传输和能源管理等问题，以提高网络的覆盖范围和性能。
6. Problem F: The Impacts of Tourism.这个问题要求团队通过建立模型，分析旅游业对一个地区的经济、环境和社会影响。
团队需要考虑游客数量、旅游收入和环境保护等因素，并给出合理的政策建议。
以上是2017年美赛的题目概述，每个题目都涉及不同的领域和问题，需要团队综合运用数学建模、数据分析和优化方法来解决。
美赛历年题目_pdf
马剑整理历年美国大学生数学建模赛题目录MCM85问题-A 动物群体的管理 (3)MCM85问题-B 战购物资储备的管理 (3)MCM86问题-A 水道测量数据 (4)MCM86问题-B 应急设施的位置 (4)MCM87问题-A 盐的存贮 (5)MCM87问题-B 停车场 (5)MCM88问题-A 确定毒品走私船的位置 (5)MCM88问题-B 两辆铁路平板车的装货问题 (6)MCM89问题-A 蠓的分类 (6)MCM89问题-B 飞机排队 (6)MCM90-A 药物在脑内的分布 (6)MCM90问题-B 扫雪问题 (7)MCM91问题-B 通讯网络的极小生成树 (7)MCM 91问题-A 估计水塔的水流量 (7)MCM92问题-A 空中交通控制雷达的功率问题 (7)MCM 92问题-B 应急电力修复系统的修复计划 (7)MCM93问题-A 加速餐厅剩菜堆肥的生成 (8)MCM93问题-B 倒煤台的操作方案 (8)MCM94问题-A 住宅的保温 (9)MCM 94问题-B 计算机网络的最短传输时间 (9)MCM-95问题-A 单一螺旋线 (10)MCM95题-B A1uacha Balaclava学院 (10)MCM96问题-A 噪音场中潜艇的探测 (11)MCM96问题-B 竞赛评判问题 (11)MCM97问题-A Velociraptor(疾走龙属)问题 (11)MCM97问题-B为取得富有成果的讨论怎样搭配与会成员 (12)MCM98问题-A 磁共振成像扫描仪 (12)MCM98问题-B 成绩给分的通胀 (13)MCM99问题-A 大碰撞 (13)MCM99问题-B “非法”聚会 (14)MCM2000问题-A空间交通管制 (14)MCM2000问题-B: 无线电信道分配 (14)MCM2001问题- A: 选择自行车车轮 (15)MCM2001问题-B 逃避飓风怒吼（一场恶风...） .. (15)MCM2001问题-C我们的水系-不确定的前景 (16)MCM2002问题-A风和喷水池 (16)MCM2002问题-B航空公司超员订票 (16)MCM2002问题-C (16)MCM2003问题-A: 特技演员 (18)MCM2003问题-B: Gamma刀治疗方案 (18)MCM2003问题-C航空行李的扫描对策 (19)MCM2004问题-A：指纹是独一无二的吗？ (19)MCM2004问题-B：更快的快通系统 (19)MCM2004问题-C安全与否？ (19)MCM2005问题A.水灾计划 (19)MCM2005B.Tollbooths (19)MCM2005问题C：不可再生的资源 (20)MCM2006问题A: 用于灌溉的自动洒水器的安置和移动调度 (20)MCM2006问题B: 通过机场的轮椅 (20)MCM2006问题C : 抗击艾滋病的协调 (21)MCM2007问题B ：飞机就座问题 (24)MCM2007问题C：器官移植：肾交换问题 (24)MCM2008问题A:给大陆洗个澡 (28)MCM2008问题B：建立数独拼图游戏 (28)MCM85问题-A 动物群体的管理在一个资源有限，即有限的食物、空间、水等等的环境里发现天然存在的动物群体。
美赛习题答案
美赛习题答案美赛习题答案在数学建模领域，美国大学生数学建模竞赛（MCM）是一项备受关注的赛事。
每年，来自全球各地的大学生们都会参与其中，挑战各种实际问题并提出解决方案。
这项竞赛不仅考察了参赛者的数学水平，更重要的是培养了他们的团队合作和创新思维能力。
本文将探讨一些典型的美赛习题，并给出相应的解答。
第一题是关于城市交通流量的问题。
题目给出了一个城市的道路网络图，要求我们计算出每条道路的平均交通量。
首先，我们可以通过收集实际交通数据来估计每条道路上的车辆数量。
然后，根据道路的长度和车辆数量，我们可以计算出每条道路的平均交通量。
最后，将结果绘制成热力图，可以清晰地显示出城市交通的拥堵情况。
第二题是关于电力系统的问题。
题目给出了一个电力系统的拓扑结构图，要求我们设计一种最优的电力传输方案，以最大化系统的可靠性和效率。
首先，我们可以使用图论的方法对电力系统进行建模，并计算出各个节点之间的电力传输路径。
然后，根据节点之间的电力传输损耗和供电能力，我们可以通过线性规划等数学方法得到最优的电力传输方案。
最后，我们可以通过模拟实验来验证我们的方案，并对其进行优化。
第三题是关于航空公司的问题。
题目给出了一家航空公司的航班数据，要求我们设计一种最优的航班调度方案，以最大化公司的利润和乘客满意度。
首先，我们可以使用图论的方法对航班网络进行建模，并计算出各个航班之间的飞行时间和成本。
然后，根据乘客的需求和航班的运营成本，我们可以通过线性规划等数学方法得到最优的航班调度方案。
最后，我们可以通过模拟实验来验证我们的方案，并对其进行优化。
以上只是美赛习题中的几个例子，实际上还有许多其他有趣的问题，涉及到经济、环境、医疗等领域。
解决这些问题需要我们具备扎实的数学基础和创新的思维能力。
在解题过程中，我们需要灵活运用数学模型和工具，结合实际情况进行分析和判断。
同时，团队合作也是解决问题的关键，每个人都应发挥自己的优势，共同努力达到最佳的解决方案。
美赛常用模型二
madio
【模型建立】 （4）从随机角度出发设计电梯运行配置方案．
④ 电梯上的10人都工作在某4个楼层，如7，8，9，10层 ，这种情况发生的概率为：
同理，电梯上的10人中无一人工作在10层（9，8，7层） 的概率均约为0.1．类似的情况共有5种．
madio
有关系式
1 2 3
l
1, 2, 3 为待定系数，为无量纲量
m
(1)的量纲表达式
[t][m ]1[l]2[g]3
T M LT 1 2 3 2 3
mg
1 0
2
3
0
2 3 1
1 0 2 1 / 2 3 1 / 2
t l g
对比
t 2 l g
madio
t ml g 1 2 3
• 方法的普适性 不需要特定的专业知识 • 结果的局限性 函数F和无量纲量未定
madio
量纲分析在物理模拟中的应用
例: 航船阻力的物理模拟
通过航船模型确定原型船所受阻力
已知模 f l3g (1,2)
型船所 受阻力
1
v gl
,
2
s l2
可得原 型船所 受阻力
f1 l13g11 (1,2)
1
v1 g1l1
madio
【模型假设】 （1）办公人员都乘电梯上楼； （2）早晨8：00以前办公人员已陆续到达一层； （3）保证每部电梯在底层等待时间内(20秒)都能达到电梯的 最大容量； （4）电梯在各层相应的停留时间内，办公人员能够完成出入 电梯的动作； （5）当无人使用电梯时，电梯在底层待命．
madio
【模型建立】 （1）电梯运行配置方案一
美赛第二次模拟题
Mathematical model of commensal and host speciesMathematical modeling of ecological unit was started by Lotka [1] and V olterra [2] and laterseveral mathematicians and ecologists Meyer [3], Kushing [4], kapur [5-6] contributed to the growth of this area ofacquaintance. The Ecological dealings can be broadly classified as Ammensalism, Competition, Commensalism,Neutralism,Mutualism, Predation and Parasitism.Here we shall consider a model of a distinctive two species commensal, host system. Commensalism is a syn biotic interaction between two populations where one population 1S is benefited by the otherpopulation 2S , while the other population 2S is neither harmed nor benefited due to interaction with the population 1S .The benefited population 1S is called the commensal and the other population 2S is called the host. For a real life example with photographs is given belowA Squirrel in an oak tree gets a place to live and food for its survival, while the tree remains neither benefited nor harmed.Consider and answer the following: Build a mathematical model to describe the two species commensal, host system. Please consult the related literature and data to complete the simulation calculation of your mathematical model.Study your model of commensalism with stochastic term which is invented and investigated the effect of environmental fluctuations around the positive equilibrium due to additive white noise.REFERENCES[1] Lotka AJ, “Elements of Physical Biology”, Williams and Wilking, Baltimore, 1925.[2] V olterra V, “Leconssen La Theorie Mathematique De La Leitte Pou Lavie”, Gauthier-Villars, Paris, 1931.[3] Meyer WJ, “Concepts of Mathematical Modeling” ,Mc. Grawhill, 1985.[4] Cushing JM, “Integro-Differential Equations and Delay Models in Population Dynamics”, Lecture Notes in Bio- Mathematics, Springerverlag,1977.[5] Kapur JN, “Mathematical Modelling in Biology and Medicine”, Affiliated East West, 1985.[6] Kapur JN, “Mathematical Modelling”, Wiley Easter, 1985.。
数学建模美赛题
2005A.水灾计划
南卡罗来纳州中部的磨累河是由北部的一个巨大水坝形成的，这是在1930年为了发电而修建的，模拟一起洪水淹没下游的事件，这起事件是由于一次灾难性的地震损毁了水坝造成的。
两个问题：
Rawls Creek是水坝下游流入Saluda河的一条终年流动的河流，则当水坝损毁后在Rawls Creek将会出现多大的洪流，洪水的波及面将有多大？
S.C.国会大厦大楼在一座小山上，在S.C.国会大厦大楼能俯视Congaree 河。
洪水能如此巨大顺流以致于水将扩展到S.C.国会大厦大楼吗？
2005B.Tollbooths(收费亭)
像Garden State Parkway，Interstate 95等等这样的长途收费公路，通常是多行道的，被分成几条高速公路，在这些高速公路上每隔一定的间隔会设立一个通行税收费广场。
因为征收通行税通常不受欢迎，所以
应该尽量减少通过通行税收费广场引起的交通混乱给汽车司机带来的烦恼。
通常，收费亭的数量要多于进入收费广场的道路的数量。
进入通行税收费广场的时候，流到大量收费亭的车辆呈扇形展开，当离开通行税收费广场的时候，车流将只能按照收费广场前行车道路的数量排队按次序通过！从而，当交通是拥挤的时，拥挤在违背通行税广场上增加。
当交通非常拥挤的时候，因为每车辆付通行费的时间要求，阻塞也会出现在通行税收费广场入口处。
建立一个模型来确定在一个容易造成阻塞的通行税收费广场中应该部署的最优的收费亭的数量。
需要保证每一个进入收费广场的交通线路上都仅有一个收费亭。
与当今的实践相比较，在什么条件下这或多或少有效？
注意："最佳"的定义由你自己决定。
2015年美国数学建模竞赛第二次模拟赛题c题
Prblem C Forest FiresOne major environmental concern is the occurrence of forest fires (also called wildfires), which affect forest preservation, bring economical and ecological damage and endanger human lives. Such phenomenon is due to multiple causes (e.g. human negligence and lightnings). Despite an increasing of state expenses to control this disaster, each year millions of forest hectares (ha) are destroyed all around the world.Fast detection is an important element for successful firefighting. Traditional human surveillance is expensive and affected by subjective factors, there has been an emphasis to develop automatic solutions, such as satellite-based, infrared/smoke scanners and local sensors (e.g. meteorological). Propagation models try to describe the future evolution of the forest fire given an initial scenario and certain input parameters. Modeling the dynamical behavior of fire propagation in a forest is helpful for creating scheme to control and fight fire.Requirement 1Describe several different metrics that could be used to evaluate the effectiveness of fire detection. Could you combine your metrics to make them even more useful for measuring quality?Requirement 2Model the dynamical behavior of fire spread in a forest.Requirement 3 Discuss the factors to affect fire occurrence. Which factors are the most critical in causing fires. Build mathematical models to predict the burned area of fires using Meteorological Data.Requirement 4 Give y our suggestion for preventing from forest fire and fighting against it.。
2021数学建模美赛题目
2021数学建模美赛题目摘要：一、引言1.介绍2021 年数学建模美赛2.分析赛题的多样性和挑战性3.强调数学建模在解决实际问题中的重要性二、2021 数学建模美赛题目概述1.A 题：新冠病毒传播的模型和控制策略2.B 题：航空公司的收益管理问题3.C 题：交通拥堵问题4.D 题：机器学习中的数据不平衡问题5.E 题：绿色供应链管理三、题目详细分析1.A 题：新冠病毒传播的模型和控制策略a.问题背景和挑战b.关键建模思路c.可能的解决方案2.B 题：航空公司的收益管理问题a.问题背景和挑战b.关键建模思路c.可能的解决方案3.C 题：交通拥堵问题a.问题背景和挑战b.关键建模思路c.可能的解决方案4.D 题：机器学习中的数据不平衡问题a.问题背景和挑战b.关键建模思路c.可能的解决方案5.E 题：绿色供应链管理a.问题背景和挑战b.关键建模思路c.可能的解决方案四、结论1.总结2021 数学建模美赛题目的特点2.强调数学建模在解决实际问题中的重要性3.对参赛者的建议和期待正文：一、引言2021 年数学建模美赛如约而至，为广大数学建模爱好者带来了一场思维的盛宴。
本届赛题涵盖了多个领域，既有新冠病毒传播的模型和控制策略，也有航空公司的收益管理问题，交通拥堵、机器学习中的数据不平衡问题和绿色供应链管理。
这些题目既具有现实意义，又具有挑战性，充分体现了数学建模在解决实际问题中的重要性。
二、2021 数学建模美赛题目概述本届数学建模美赛共有五个题目，分别是：1.A 题：新冠病毒传播的模型和控制策略2.B 题：航空公司的收益管理问题3.C 题：交通拥堵问题4.D 题：机器学习中的数据不平衡问题5.E 题：绿色供应链管理三、题目详细分析1.A 题：新冠病毒传播的模型和控制策略a.问题背景和挑战：新冠病毒的传播给全球带来了严重的公共卫生危机。
预测病毒传播、评估控制策略的有效性是解决这一问题的关键。
本题要求建立新冠病毒传播模型，分析控制策略对疫情发展的影响。
2020年美赛试题
2020年美赛试题全文共四篇示例，供读者参考第一篇示例：2022020年美赛试题是一个国际性的数学建模比赛，是美国大学生数学建模竞赛的简称。
该比赛每年都吸引着全球众多优秀的大学生数学爱好者参与，旨在培养学生的团队合作能力、数学建模能力和解决实际问题的能力。
2020年美赛试题包括了多个实际问题，涉及到各种不同领域的知识和技能。
有关气候变化、交通拥堵、疾病传播等方面的问题，都是参赛选手需要解决的挑战。
参赛选手需要在规定的时间内，对所选题目进行深入分析、建立数学模型、进行模拟计算，并最终给出合理有效的解决方案。
本次比赛的试题设计十分考验参赛选手的综合能力，要求他们具备较强的数学建模能力、编程能力、数据分析能力等。
参赛选手需要充分发挥团队合作精神，共同分工协作，共同完成试题，最终得出科学合理的结论。
除了在数学建模能力上的要求，参赛选手还需要具备良好的逻辑思维能力、创新能力和团队精神。
在解决实际问题的过程中，需要他们不断挑战自我，勇于探索未知领域，寻找新的解决方案。
在本次比赛中，参赛选手将会面临着各种各样的挑战和困难。
他们需要面对未知的实际问题，需要分析复杂的数据，需要精确建立数学模型，需要进行大量的模拟计算。
只有克服了这些困难，才能最终给出可信的解决方案。
2020年美赛试题的设计十分贴近实际生活，涉及到了各种领域的知识，对参赛选手提出了很高的要求。
参赛选手需要在短时间内做出合理的数学建模、给出有效的解决方案，这不仅考验了他们的数学水平，更考验了他们的团队合作能力和解决问题的能力。
通过参与这样的数学建模比赛，不仅可以提高参赛选手的综合素质，更可以锻炼他们的团队合作精神和解决问题的能力。
希望更多的大学生能够参与到类似的比赛中，不断挑战自我，不断提高自己的能力，成为未来社会的栋梁之才。
第二篇示例：2020年美国大学生数学建模竞赛（简称美赛）是一项旨在提倡学生团队合作、数学建模和创新思维的竞赛活动。
该赛事已经成为全球最具影响力的数学建模比赛之一，吸引了来自世界各地的大学生参与。