美国中学生数学建模
美国数学建模大赛比赛规则
数学中国MCM/ICM参赛指南翻译（2014版）MCM:The Mathematical Contest in ModelingMCM：数学建模竞赛ICM:The InterdisciplinaryContest in ModelingICM：交叉学科建模竞赛ContestRules, Registration and Instructions 比赛规则，比赛注册方式和参赛指南(All rules and instructions apply to both ICM and MCMcontests, except where otherwisenoted.)（所有MCM的说明和规则除特别说明以外都适用于ICM）每个MCM的参赛队需有一名所在单位的指导教师负责。
指导老师：请认真阅读这些说明，确保完成了所有相关的步骤。
每位指导教师的责任包括确保每个参赛队正确注册并正确完成参加MCM/ ICM所要求的相关步骤。
请在比赛前做一份《参赛指南》的拷贝，以便在竞赛时和结束后作为参考。
组委会很高兴宣布一个新的补充赛事（针对MCM/ICM 比赛的视频录制比赛）。
点击这里阅读详情！1.竞赛前A．注册B．选好参赛队成员2.竞赛开始之后A．通过竞赛的网址查看题目B．选题C．参赛队准备解决方案D．打印摘要和控制页面3.竞赛结束之前A.发送电子版论文。
4.竞赛结束的时候,A. 准备论文邮包B．邮寄论文5.竞赛结束之后A. 确认论文收到B．核实竞赛结果C．发证书D．颁奖I. BEFORE THE CONTEST BEGINS:（竞赛前）A.注册所有的参赛队必须在美国东部时间2014年2月6号（星期四）下午2点前完成注册。
届时，注册系统将会自动关闭，不再接受新的注册。
任何未在规定时间内注册的参赛队都没有参加2014年MCM/ICM的资格。
不存在例外情况。
1参赛队通过下面的网站在线注册：/undergraduate/contests/m cma.如果您刚刚开始注册竞赛的第一个参赛队，请点击网页左边的Register for2014 Contest 。
历届美国数学建模竞赛赛题(汉语版)
历届美国数学建模竞赛赛题, 1985－2006AMCM1985问题-A 动物群体的管理AMCM1985问题-B 战购物资储备的管理AMCM1986问题-A 水道测量数据AMCM1986问题-B 应急设施的位置AMCM1987问题-A 盐的存贮AMCM1987问题-B 停车场AMCM1988问题-A 确定毒品走私船的位置AMCM1988问题-B 两辆铁路平板车的装货问题AMCM1989问题-A 蠓的分类AMCM1989问题-B 飞机排队AMCM1990问题-A 药物在脑内的分布AMCM1990问题-B 扫雪问题AMCM1991问题-A 估计水塔的水流量AMCM1992问题-A 空中交通控制雷达的功率问题AMCM1992问题-B 应急电力修复系统的修复计划AMCM1993问题-A 加速餐厅剩菜堆肥的生成AMCM1993问题-B 倒煤台的操作方案AMCM1994问题-A 住宅的保温AMCM1994问题-B 计算机网络的最短传输时间AMCM1995问题-A 单一螺旋线AMCM1995问题-B A1uacha Balaclava学院AMCM1996问题-A 噪音场中潜艇的探测AMCM1996问题-B 竞赛评判问题AMCM1997问题-A Velociraptor(疾走龙属)问题AMCM1997问题-B为取得富有成果的讨论怎样搭配与会成员AMCM1998问题-A 磁共振成像扫描仪AMCM1998问题-B 成绩给分的通胀AMCM1999问题-A 大碰撞AMCM1999问题-B “非法”聚会AMCM1999问题- C 大地污染AMCM2000问题-A空间交通管制AMCM2000问题-B: 无线电信道分配AMCM2000问题-C：大象群落的兴衰AMCM2001问题- A: 选择自行车车轮AMCM2001问题-B：逃避飓风怒吼（一场恶风…）AMCM2001问题-C我们的水系-不确定的前景AMCM2002问题-A风和喷水池AMCM2002问题-B航空公司超员订票AMCM2002问题-C蜥蜴问题AMCM2003问题-A: 特技演员AMCM2003问题-C航空行李的扫描对策AMCM2004问题-A：指纹是独一无二的吗？AMCM2004问题-B：更快的快通系统AMCM2004问题-C：安全与否？AMCM2005问题-A：.水灾计划AMCM2005问题-B：TollboothsAMCM2005问题-C：.Nonrenewable ResourcesAMCM2006问题-A：用于灌溉的自动洒水器的安置和移动调度AMCM2006问题-B：通过机场的轮椅AMCM2006问题-C：在与HIV/爱滋病的战斗中的交易AMCM85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。
3分钟完整了解·HiMCM美国高中生数学建模竞赛
眼看一年一度的美国高中生数学建模竞赛就要到来了，聪明机智的你准备好了吗？今年和码趣学院一起去参加吧！什么是HiMCMHiMCM(High School Mathematical Contest in Modeling)美国高中生数学建模竞赛，是美国数学及其应用联合会（COMAP）主办的活动，面向全球高中生开放。
竞赛始于1999年，大赛组委将现实生活中的各种问题作为赛题，通过比赛来考验学生的综合素质。
HiMCM不仅需要选手具备编程技巧，更强调数学，逻辑思维和论文写作能力。
这项竞赛是借鉴了美国大学生数学建模竞赛的模式，结合中学生的特点进行设计的。
为什么要参加HiMCM数学逻辑思维是众多学科的基础，在申请高中或大学专业的时候（如数学，经济学，计算机等），参加了优质的数学竞赛的经历都会大大提升申请者的学术背景。
除了AMC这种书面数学竞赛，在某种程度上数学建模更能体现学生用数学知识解决各种问题的能力。
比赛形式注意：HiMCM比赛可远程参加，无规定的比赛地点，无需提交纸质版论文。
重要的是参赛者应注重解决方案的设计性，表述的清晰性。
1.参赛队伍在指定17天中，选择连续的36小时参加比赛。
2.比赛开始后，指导教师可登陆相应的网址查看赛题，从A题或B题中任选其一。
3.在选定的36小时之内，可以使用书本、计算机和网络，但不能和团队以外的任何人员交流（包括本队指导老师）比赛题目1.比赛题目来自现实生活中的两个真实的问题，参赛队伍从两个选题中任选一个。
比赛题目为开放性的，没有唯一的解决方案。
2.赛事组委会的评审感兴趣的是参赛队伍解决问题的方法，所以不太完整的解决方案也能提交。
3.参赛队伍必须将问题的解决方案整理成31页内的学术论文（包括一页摘要），学术论文中可以用图表，数据等形式，支撑问题的解决方案4.赛后，参赛队伍向COMPA递交学术论文，最终成果以英文报告的方式，通过电子邮件上传。
表彰及奖励参赛队伍的解决方案由COMPA组织专家评阅，最后评出：特等奖（National Outstanding)特等奖提名奖（National Finalist or Finalist）一等奖（Meritorious)二等奖（Honorable Mentioned)成功参与奖（Successful Participate)为鼓励参赛学生的积极性，每个交卷的参赛队至少可以获得成功参赛奖，而获二等奖以上的参赛队一般占总参赛队的60%左右，获得特等奖团队每年不超过参赛队总数的1%。
附录1 中、美数学建模竞赛简介
附录1 中、美数学建模竞赛简介1.美国大学生数学建模竞赛简介现代社会的发展，需要数学理论与方法，但更需要熟悉各种数学方法，能够与物理学家、工程师等合作，能够解决实际问题的专家。
这种需要也必然引起大学数学教学的改革。
例如，英国牛津大学就有数学建模方面的博士点，而美国人直接将数学建模课引入理科、理工科大学生的教学中，并设立了一年一次的“大学生数学建模竞赛”，简记为MCM 。
这个竞赛吸引了许多国家的大学派队参赛，其国际影响及权威性日益增加。
美国为此有专门的刊物，比如《数学和计算机建模》：Mathematical and Computer Modelling ─ An International Journal ，缩写为 put.Modelling ，1980年创刊，开始是季刊，很快改为月刊。
1988年以前刊名叫Mathematical Modelling ─ An International Journal ，每隔一年出一次国际数学建模会议纪要（近1000页）作为增刊。
我国部分高校从1989年开始组队参赛，并取得很好的成绩。
美国大学生MCM 有如下特点：(1) 参赛队都必须事先报名注册，每个系至多2个队，每队3名队员。
参赛队员直到比赛前一分钟都可换掉，而不必通知竞赛委员会（COMAP ）；若某系事先只报名注册一个队，赛前想再增加一个队，则必须事先从COMAP 取得考号（ Control Number ）才行；(2) 每个队都将收到2个考题：A 、B ，由参赛队3个队员任选一题给出解答。
一旦选定考题，队员不得与教练或其他无关人员讨论题目的解答等相关事宜。
参赛队可使用计算机、软件包、图书馆等类似工具参加竞赛。
参赛题目不一定有解答（如猜想等），若有答案，也不一定是唯一答案。
例如86-B 紧急救援机构选址问题：Steiner 树问题：平面上有m 个点p1、p2、…、pm ，再加一点p 向pi (1≤i ≤m)连直线，使得连线总长最短，如何求p ？这是一个NP-Complete 问题。
写给要参加美国数学建模竞赛的同学
写给要参加美国数学建模竞赛的同学美国赛分为两种：MCM(The Mathematical Contest in Modeling数学建模竞赛)和ICM(Interdisciplinary Contest in Modeling跨学科建模竞赛)。
每年的美国赛共有A,B,C三个题，如果选做MCM竞赛，那么就在A,B题中选一题做，如果做ICM竞赛，那么就只能做C题。
美国赛一般是在每年的二月中旬举行，2011年的美国赛将在在2011年2月10号到14号举行。
MCM规定，一名指导老师最多只能指导两个队，每个队的报名费为100美元。
美国赛面向全球，通过网络报名，参赛队交费通过via Mastercard or Visa卡通过网络支付，交费报名的同时取得唯一的报名号。
竞赛期间的规则和全国赛差不多，这里不再重复。
美国赛参赛队需要将paper和summary准备三份在deadline之前寄到组委会。
三份论文会分别交给：The Institute for Operations Research and the Management Sciences (美国运筹与管理科学协会)，The Society for Industrial and Applied Mathematics（美国工业与应用数学协会），The Mathematical Association of America（美国数学会）进行评选，summary及其重要， summary写的出色，judge通过你的summary才能产生进一步看你paper欲望，因为参加竞赛的team是很多的，summary不能打动judge,那么直接就被fire掉了。
每个协会的judge会给每个题中评出的一个outstanding奖得主（有时一个题最后评出来的outstanding 奖有5，6支队，但是这三个协会都只会提名一只队伍作为该协会的outstanding奖得主，有时还会出现一只队获得了两个协会的同时提名的情况:06年做A题的科罗拉多大学的一个队就同时获得了美国工业与应用数学协会和美国数学会的提名。
美国数学建模竞赛题目(1985--2009年)
美国数学建模竞赛题目1985年：A题：动物群体的管理B题：战略物资储备的管理问题1986年：A题：海底地型测量问题B题：应急设施的优化选址问题1987年：A题：堆盐问题（盐堆稳定性问题）B题：停车场安排问题1988年：A题：确定毒品走私船位置B题：平板列车车厢的优化装载1989年：A题：蠓虫识别问题；最佳分类与隔离B题：飞机排队模型1990年：A题：脑中多巴胺的分布B题：铲雪车的路径与效率问题1991年：A题：估计水塔的水流量B题：通信网络费用问题1992年：A题：雷达系统的功率与设计式样B题：紧急修复系统的研制1993年：A题：堆肥问题B题：煤炭装卸场的最优操作1994年：A题：保温房屋设计问题B题：计算机网络的最小接通时间1996年：A题：大型水下物体的探测B题：快速遴选优胜者问题1997年：A题：恐龙捕食问题B题：会议混合安排问题1998年：A题：MRI图象处理问题B题：分数贬值问题1999年：A题：小星体撞击地球问题B题：公用设施的合法容量问题C题：确定环境污染的物质、位置、数量和时间的问题2000年：A题：空间交通管制B题：无线电信道分配C题：大象群落的兴衰2001年：A题：选择自行车车轮B题：逃避飓风怒吼C题：我们的水系-不确定的前景2002年：A题：风和喷水池B题：航空公司超员订票C题：如果我们过分扫荡自己的土地，将会失去各种各样的蜥蜴。
2003年：A题：特技演员B题：Gamma刀治疗方案C题：航空行李的扫描对策2004年：A题：指纹是独一无二的吗？B题：更快的快通系统C题：安全与否？2005年：A题：flood planningB题：tollboothsC题: Nonrenewable Resources2006年：A题：Positioning and Moving SprinklerSystems for IrrigationB题：Wheel Chair Access at AirportsC题：Trade-offs in the fight againstHIV/AIDS2007年：A题：GerrymanderingB题：The Airplane Seating ProblemC题：Organ Transplant: The Kidney Exchange Problem2008年：A题：Take a BathB题：Creating Sudoku PuzzlesC题：Finding the Good in Health Care Systems2009年：A题：Designing a Traffic CircleB题：Energy and the Cell PhoneC题：Creating Food Systems: Re-Balancing Human-Influenced Ecosystems。
HIMCM 2014美国中学生数学建模竞赛试题
HIMCM 2014美国中学生数学建模竞赛试题Problem A: Unloading Commuter TrainsTrains arrive often at a central Station, the nexus for many commuter trains from suburbs of larger cities on a “commuter” line. Most trains are long (perhaps 10 or more cars long). The distance a passenger has to walk to exit the train area is quite long. Each train car has only two exits, one near each end so that the cars can carry as many people as possible. Each train car has a center aisle and there are two seats on one side and three seats on the other for each row of seats.To exit a typical station of interest, passengers must exit the car, and then make their way to a stairway to get to the next level to exit the station. Usually these trains are crowded so there is a “fan” of passengers from the train trying to get up the stairway. The stairway could accommodate two columns of people exiting to the top of the stairs.Most commuter train platforms have two tracks adjacent to the platform. In the worst case, if two fully occupied trains arrived at the same time, it might take a long time for all the passengers to get up to the main level of the station.Build a mathematical model to estimate the amount of time for a passenger to reach the street level of the station to exit the complex. Assume there are n cars to a train, each car has length d. The length of the platform is p, and the number of stairs in each staircase is q. Use your model to specifically optimize (minimize) the time traveled to reach street level to exit a station for the following:问题一:通勤列车的负载问题在中央车站,经常有许多的联系从大城市到郊区的通勤列车“通勤”线到达。
美国数学建模技巧
一、实际问题一数学问题一数学解一实际问题的解决．如果你只重视其中一个过程(一般初参赛的时候容易犯这个错误)，而对第一和第三这两个过程不予重视，那就违背了放学建模竞赛的宗旨，当然就不能得到好的结果．为什么要叫数学建模竞赛?就是因为它比的是建立数学模型，而不只是比赛解答数学模型．二、在数学建模学习中一般应注意的几个方面(1)要深刻领会数学的重要性不仅体现在数学知识的应用，更重要的是数学的思维方法，这暇包括思考问题的方式，所运用的数学方法及处理技巧等，特别应致力于“双向"翻译、逻辑推理、联想和洞察四种基本能力的培养．(2)要提高动手能力，这包括自学、文献检索、计算机应用、科技论文写作和相互交流能力，特别应有意识地增强文字表述方面的准确性和简明性．(3)要勇于克服学习中的困难，消除畏难情绪．由于数学建模课程属于拓宽性的、启发性强的、难度较深的课程，它提倡创造性思维方法的训练，因而文字习题解题中找不到感觉或做得有出入是一种正常现象，对此不必丧失信心．相信通过摸索会逐步有所改进，如能解决好几个问题或真正动手完成一两个实际题目都应视为有所收获．从长远看这种学习有益于开阔人们的思路和眼界，有利于知识结构的改善和综合素质的提高．三、一、竞赛参考书l、中国大学生数学建模竞赛，李大潜主编，高等教育出版社(1998)．2、大学生数学建模竞赛辅导教材，(一)(二)(三)，叶其孝主编，湖南教育出版社(1993，1997，1998)．3、数学建模教育与国际数学建模竞赛《工科数学》专辑，叶其孝主编，《工科数学》杂志社，1994)．二、国内教材、丛书：1、数学模型，姜启源编，高等教育出版社(1987年第一版，1993年第二版；第一版在1992年国家教委举办的第二届全国优秀教材评选中获"全国优秀教材奖")．2、数学模型与计算机模拟，江裕钊、辛培情编，电子科技大学出版社，(1989)．3、数学模型选谈(走向数学从书)，华罗庚，王元著，王克译，湖南教育出版社；(1991)．4、数学建模--方法与范例，寿纪麟等编，西安交通大学出版社(1993)．5、数学模型，濮定国、田蔚文主编，东南大学出版社(1994)．6..数学模型，朱思铭、李尚廉编，中山大学出版社，(1995)7、数学模型，陈义华编著，重庆大学出版社，(1995)8、数学模型建模分析，蔡常丰编著，科学出版社，(1995)．9、数学建模竞赛教程，李尚志主编，江苏教育出版社，(1996)．10、数学建模入门，徐全智、杨晋浩编，成都电子科大出版社，(1996).11、数学建模，沈继红、施久玉、高振滨、张晓威编，哈尔滨工程大学出版社，(1996).12、数学模型基础，王树禾编著，中国科学技术大学出版社，(1996).13、数学模型方法，齐欢编著，华中理工大学出版社，(1996)．14、数学建模与实验，南京地区工科院校数学建模与工业数学讨论班编，河海大学出版社，(1996)．15、数学模型与数学建模，刘来福、曾文艺编，北京师范大学出版杜(1997)．16. 数学建模，袁震东、洪渊、林武忠、蒋鲁敏编，华东师范大学出版社.17、数学模型，谭永基，俞文吡编，复旦大学出版社，(1997)．18、数学模型实用教程，费培之、程中瑗层主编，四川大学出版社，(1998)．19、数学建模优秀案例选编(工科数学基地建设丛书)，汪国强主编，华南理工大学出版社，(1998)．20、经济数学模型(第二版)(工科数学基地建设丛书)，洪毅、贺德化、昌志华编著，华南理工大学出版社，(1999)．21、数学模型讲义，雷功炎编，北京大学出版社(1999)．22、数学建模精品案例，朱道元编著，东南大学出版社，(1999)，23、问题解决的数学模型方法，刘来福，曾文艺编著、北京师范大学出版社，(1999)．24、数学建模的理论与实践，吴翔，吴孟达，成礼智编著，国防科技大学出版社，(1999)．25、数学建模案例分析，白其岭主编，海洋出版社，(2000年，北京)．26、数学实验(高等院校选用教材系列)，谢云荪、张志让主编，科学出版社，(2000)．27、数学实验，傅鹏、龚肋、刘琼荪，何中市编，科学出版社，(2000)．三、国外参考书(中译本)：1、数学模型引论，E．A。
2012年美国高中生数学建模竞赛特等奖论文
题目：How Much Gas Should I Buy This Week?题目来源：2012年第十五届美国高中生数学建模竞赛（HiMCM）B题获奖等级：特等奖，并授予INFORMS奖论文作者：深圳中学2014届毕业生李依琛、王喆沛、林桂兴、李卓尔指导老师：深圳中学张文涛AbstractGasoline is the bleed that surges incessantly within the muscular ground of city; gasoline is the feast that lures the appetite of drivers. “To fill or not fill?” That is the question flustering thousands of car owners. This paper will guide you to predict the gasoline prices of the coming week with the currently available data with respect to swift changes of oil prices. Do you hold any interest in what pattern of filling up the gas tank can lead to a lower cost in total?By applying the Time series analysis method, this paper infers the price in the imminent week. Furthermore, we innovatively utilize the average prices of the continuous two weeks to predict the next two week’s average price; similarly, employ the four-week-long average prices to forecast the average price of four weeks later. By adopting the data obtained from 2011and the comparison in different aspects, we can obtain the gas price prediction model ：G t+1=0.0398+1.6002g t+−0.7842g t−1+0.1207g t−2+ 0.4147g t−0.5107g t−1+0.1703g t−2+ε .This predicted result of 2012 according to this model is fairly ideal. Based on the prediction model,We also establish the model for how to fill gasoline. With these models, we had calculated the lowest cost of filling up in 2012 when traveling 100 miles a week is 637.24 dollars with the help of MATLAB, while the lowest cost when traveling 200 miles a week is 1283.5 dollars. These two values are very close to the ideal value of cost on the basis of the historical figure, which are 635.24 dollars and 1253.5 dollars respectively. Also, we have come up with the scheme of gas fulfillment respectively. By analyzing the schemes of gas filling, we can discover that when you predict the future gasoline price going up, the best strategy is to fill the tank as soon as possible, in order to lower the gas fare. On the contrary, when the predicted price tends to decrease, it is wiser and more economic for people to postpone the filling, which encourages people to purchase a half tank of gasoline only if the tank is almost empty.For other different pattern for every week’s “mileage driven”, we calculate the changing point of strategies-changed is 133.33 miles.Eventually, we will apply the models -to the analysis of the New York City. The result of prediction is good enough to match the actual data approximately. However, the total gas cost of New York is a little higher than that of the average cost nationally, which might be related to the higher consumer price index in the city. Due to the limit of time, we are not able to investigate further the particular factors.Keywords: gasoline price Time series analysis forecast lowest cost MATLABAbstract ---------------------------------------------------------------------------------------1 Restatement --------------------------------------------------------------------------------------21. Assumption----------------------------------------------------------------------------------42. Definitions of Variables and Models-----------------------------------------------------4 2.1 Models for the prediction of gasoline price in the subsequent week------------4 2.2 The Model of oil price next two weeks and four weeks--------------------------5 2.3 Model for refuel decision-------------------------------------------------------------52.3.1 Decision Model for consumer who drives 100 miles per week-------------62.3.2 Decision Model for consumer who drives 200 miles per week-------------73. Train and Test Model by 2011 dataset---------------------------------------------------8 3.1 Determine the all the parameters in Equation ② from the 2011 dataset-------8 3.2 Test the Forecast Model of gasoline price by the dataset of gasoline price in2012-------------------------------------------------------------------------------------10 3.3 Calculating ε --------------------------------------------------------------------------12 3.4 Test Decision Models of buying gasoline by dataset of 2012-------------------143.4.1 100 miles per week---------------------------------------------------------------143.4.2 200 miles per week---------------------------------------------------------------143.4.3 Second Test for the Decision of buying gasoline-----------------------------154. The upper bound will change the Decision of buying gasoline---------------------155. An analysis of New York City-----------------------------------------------------------16 5.1 The main factor that will affect the gasoline price in New York City----------16 5.2 Test Models with New York data----------------------------------------------------185.3 The analysis of result------------------------------------------------------------------196. Summery& Advantage and disadvantage-----------------------------------------------197. Report----------------------------------------------------------------------------------------208. Appendix------------------------------------------------------------------------------------21 Appendix 1(main MATLAB programs) ------------------------------------------------21 Appendix 2(outcome and graph) --------------------------------------------------------34The world market is fluctuating swiftly now. As the most important limited energy, oil is much accounted of cars owners and dealer. We are required to make a gas-buying plan which relates to the price of gasoline, the volume of tank, the distance that consumer drives per week, the data from EIA and the influence of other events in order to help drivers to save money.We should use the data of 2011 to build up two models that discuss two situations: 100miles/week or 200miles/week and use the data of 2012 to test the models to prove the model is applicable. In the model, consumer only has three choices to purchase gas each week, including no gas, half a tank and full tank. At the end, we should not only build two models but also write a simple but educational report that can attract consumer to follow this model.1.Assumptiona)Assume the consumer always buy gasoline according to the rule of minimumcost.b)Ignore the difference of the gasoline weight.c)Ignore the oil wear on the way to gas stations.d)Assume the tank is empty at the beginning of the following models.e)Apply the past data of crude oil price to predict the future price ofgasoline.(The crude oil price can affect the gasoline price and we ignore thehysteresis effect on prices of crude oil towards prices of gasoline.)2.Definitions of Variables and Modelst stands for the sequence number of week in any time.(t stands for the current week. (t-1) stands for the last week. (t+1) stands for the next week.c t: Price of crude oil of the current week.g t: Price of gasoline of the t th week.P t: The volume of oil of the t th week.G t+1: Predicted price of gasoline of the (t+1)th week.α,β: The coefficient of the g t and c t in the model.d: The variable of decision of buying gasoline.(d=1/2 stands for buying a half tank gasoline)2.1 Model for the prediction of gasoline price in the subsequent weekWhether to buy half a tank oil or full tank oil depends on the short-term forecast about the gasoline prices. Time series analysis is a frequently-used method to expect the gasoline price trend. It can be expressed as:G t+1=α1g t+α2g t−1+α3g t−2+α4g t−3+…αn+1g t−n+ε ----Equation ①ε is a parameter that reflects the influence towards the trend of gasoline price in relation to several aspects such as weather data, economic data, world events and so on.Due to the prices of crude oil can influence the future prices of gasoline; we will adopt the past prices of crude oil into the model for gasoline price forecast.G t+1=(α1g t+α2g t−1+α3g t−2+α4g t−3+⋯αn+1g t−n)+(β1g t+β2g t−1+β3g t−2+β4g t−3+⋯βn+1g t−n)+ε----Equation ②We will use the 2011 data set to calculate the all coefficients and the best delay periods n.2.2 The Model of oil price next two weeks and four weeksWe mainly depend on the prediction of change of gasoline price in order to make decision that the consumer should buy half a tank or full tank gas. When consumer drives 100miles/week, he can drive whether 400miles most if he buys full tank gas or 200miles most if he buys half a tank gas. When consumer drives 200miles/week, full tank gas can be used two weeks most or half a tank can be used one week most. Thus, we should consider the gasoline price trend in four weeks in future.Equation ②can also be rewritten asG t+1=(α1g t+β1g t)+(α2g t−1+β2g t−1)+(α3g t−2+β3g t−2)+⋯+(αn+1g t−n+βn+1g t−n)+ε ----Equation ③If we define y t=α1g t+β1g t,y t−1=α2g t−1+β2g t−1, y t−2=α3g t−2+β3g t−2……, and so on.Equation ③can change toG t+1=y t+y t−1+y t−2+⋯+y t−n+ε ----Equation ④We use y(t−1,t)denote the average price from week (t-1) to week (t), which is.y(t−1,t)=y t−1+y t2Accordingly, the average price from week (t-3) to week (t) isy(t−3,t)=y t−3+y t−2+y t−1+y t.4Apply Time series analysis, we can get the average price from week (t+1) to week (t+2) by Equation ④,G(t+1,t+2)=y(t−1,t)+y(t−3,t−2)+y(t−5,t−4), ----Equation ⑤As well, the average price from week (t+1) to week (t+4) isG(t+1,t+4)=y(t−3,t)+y(t−7,t−4)+y(t−11,t−8). ----Equation ⑥2.3 Model for refuel decisionBy comparing the present gasoline price with the future price, we can decide whether to fill half or full tank.The process for decision can be shown through the following flow chart.Chart 1For the consumer, the best decision is to get gasoline with the lowest prices. Because a tank of gasoline can run 2 or 4 week, so we should choose a time point that the price is lowest by comparison of the gas prices at present, 2 weeks and 4 weeks later separately. The refuel decision also depends on how many free spaces in the tank because we can only choose half or full tank each time. If the free spaces are less than 1/2, we can refuel nothing even if we think the price is the lowest at that time.2.3.1 Decision Model for consumer who drives 100 miles per week.We assume the oil tank is empty at the beginning time(t=0). There are four cases for a consumer to choose a best refuel time when the tank is empty.i.g t>G t+4and g t>G t+2, which means the present gasoline price is higherthan that either two weeks or four weeks later. It is economic to fill halftank under such condition. ii. g t <Gt +4 and g t <G t +2, which means the present gasoline price is lower than that either two weeks or four weeks later. It is economic to fill fulltank under such condition. iii. Gt +4>g t >G t +2, which means the present gasoline price is higher than that two weeks later but lower than that four weeks later. It is economic to fillhalf tank under such condition. iv. Gt +4<g t <G t +2, which means the present gasoline price is higher than that four weeks later but lower than that two weeks later. It is economic to fillfull tank under such condition.If other time, we should consider both the gasoline price and the oil volume in the tank to pick up a best refuel time. In summary, the decision model for running 100 miles a week ist 2t 4t 2t 4t 2t 4t 2t 4t 11111411111ˆˆ(1)1((1)&max(,))24442011111ˆˆˆˆ1/2((1)&G G G (&))(0(1G G )&)4424411ˆˆˆ(1)0&(G 4G G (G &)t i t i t t t t i t i t t t t t t i t t d t or d t g d d t g or d t g d t g or ++++----+++-++<--<<--<>⎧⎪=<--<<<--<<<⎨⎪⎩--=><∑∑∑∑∑t 2G ˆ)t g +<----Equation ⑦d i is the decision variable, d i =1 means we fill full tank, d i =1/2 means we fill half tank. 11(1)4t i tdt ---∑represents the residual gasoline volume in the tank. The method of prices comparison was analyzed in the beginning part of 2.3.1.2.3.2 Decision Model for consumer who drives 200 miles per week.Because even full tank can run only two weeks, the consumer must refuel during every two weeks. There are two cases to decide whether to buy half or full tank when the tank is empty. This situation is much simpler than that of 100 miles a week. The process for decision can also be shown through the following flow chart.Chart 2The two cases for deciding buy half or full tank are: i. g t >Gt +1, which means the present gasoline price is higher than the next week. We will buy half tank because we can buy the cheaper gasoline inthe next week. ii. g t <Gt +1, which means the present gasoline price is lower than the next week. To buy full tank is economic under such situation.But we should consider both gasoline prices and free tank volume to decide our refueling plan. The Model is111t 11t 111(1)1220111ˆ1/20(1)((1)0&)22411ˆ(1&G )0G 2t i t t i t i t t t t t i t t d t d d t or d t g d t g ----++<--<⎧⎪=<--<--=>⎨⎪⎩--=<∑∑∑∑ ----Equation ⑧3. Train and Test Model by the 2011 datasetChart 33.1 Determine all the parameters in Equation ② from the 2011 dataset.Using the weekly gas data from the website and the weekly crude price data from , we can determine the best delay periods n and calculate all the parameters in Equation ②. For there are two crude oil price dataset (Weekly Cushing OK WTI Spot Price FOB and Weekly Europe Brent SpotPrice FOB), we use the average value as the crude oil price without loss of generality. We tried n =3, 4 and 5 respectively with 2011 dataset and received comparison graph of predicted value and actual value, including corresponding coefficient.（A ） n =3(the hysteretic period is 3)Graph 1 The fitted price and real price of gasoline in 2011(n=3)We find that the nearby effect coefficient of the price of crude oil and gasoline. This result is same as our anticipation.（B）n=4(the hysteretic period is 4)Graph 2 The fitted price and real price of gasoline in 2011（n=4）(C) n=5(the hysteretic period is 5)Graph 3 The fitted price and real price of gasoline in 2011（n=5）Via comparing the three figures above, we can easily found that the predictive validity of n=3(the hysteretic period is 3) is slightly better than that of n=4(the hysteretic period is 4) and n=5(the hysteretic period is 5) so we choose the model of n=3 to be the prediction model of gasoline price.G t+1=0.0398+1.6002g t+−0.7842g t−1+0.1207g t−2+ 0.4147g t−0.5107g t−1+0.1703g t−2+ε----Equation ⑨3.2 Test the Forecast Model of gasoline price by the dataset of gasoline price in 2012Next, we apply models in terms of different hysteretic periods(n=3,4,5 respectively), which are shown in Equation ②,to forecast the gasoline price which can be acquired currently in 2012 and get the graph of the forecast price and real price of gasoline:Graph 4 The real price and forecast price in 2012（n=3）Graph 5 The real price and forecast price in 2012（n=4）Graph 6 The real price and forecast price in 2012（n=5）Conserving the error of observation, predictive validity is best when n is 3, but the differences are not obvious when n=4 and n=5. However, a serious problem should be drawn to concerns: consumers determines how to fill the tank by using the trend of oil price. If the trend prediction is wrong (like predicting oil price will rise when it actually falls), consumers will lose. We use MATLAB software to calculate the amount of error time when we use the model of Equation ⑨to predict the price of gasoline in 2012. The graph below shows the result.It’s not difficult to find the prediction effect is the best when n is 3. Therefore, we determined to use Equation ⑨as the prediction model of oil price in 2012.G t+1=0.0398+1.6002g t+−0.7842g t−1+0.1207g t−2+ 0.4147g t−0.5107g t−1+0.1703g t−2+ε3.3 Calculating εSince political occurences, economic events and climatic changes can affect gasoline price, it is undeniable that a ε exists between predicted prices and real prices. We can use Equation ②to predict gasoline prices in 2011 and then compare them with real data. Through the difference between predicted data and real data, we can estimate the value of ε .The estimating process can be shown through the following flow chartChart 4We divide the international events into three types: extra serious event, major event and ordinary event according to the criteria of influence on gas prices. Then we evaluate the value: extra serious event is 3a, major event is 2a, and ordinary event is a. With inference to the comparison of the forecast price and real price in 2011, we find that large deviation of data exists at three time points: May 16,2011, Aug 08,2011 andOct 10,2011. After searching, we find that some important international events happened nearly at the three time points. We believe that these events which occurred by chance affect the international prices of gasoline so the predicted prices deviate from the actual prices. The table of events and the calculation of the value of a areTherefore, by generalizing several sets of particular data and events, we can estimate the value of a:a=26.84 ----Equation ⑩The calculating process is shown as the following graph.Since now we have obtained the approximate value of a, we can evaluate the future prices according to currently known gasoline prices and crude oil prices. To improve our model, we can look for factors resulting in some major turning point in the graph of gasoline prices. On the ground that the most influential factors on prices in 2012 are respectively graded, the difference between fact and prediction can be calculated.3.4 Test Decision Models of buying gasoline by the dataset of 2012First, we use Equation ⑨to calculate the gasoline price of next week and use Equation ⑤and Equation ⑥to calculate the gasoline price trend of next two to four weeks. On the basis above, we calculate the total cost, and thus receive schemes of buying gasoline of 100miles per week according to Equation ⑦and Equation ⑧. Using the same method, we can easily obtain the pattern when driving 200 miles per week. The result is presented below.We collect the important events which will affect the gasoline price in 2012 as well. Therefore, we calculate and adjust the predicted price of gasoline by Equation ⑩. We calculate the scheme of buying gasoline again. The result is below:3.4.1 100 miles per weekT2012 = 637.2400 （If the consumer drives 100 miles per week, the total cost inTable 53.4.2 200 miles per weekT2012 = 1283.5 （If the consumer drives 200 miles per week, the total cost in 2012 is 1283.5 USD）. The scheme calculated by software is below：Table 6According to the result of calculating the buying-gasoline scheme from the model, we can know: when the gasoline price goes up, we should fill up the tank first and fill up again immediately after using half of gasoline. It is economical to always keep the tank full and also to fill the tank in advance in order to spend least on gasoline fee. However, when gasoline price goes down, we have to use up gasoline first and then fill up the tank. In another words, we need to delay the time of filling the tank in order to pay for the lowest price. In retrospect to our model, it is very easy to discover that the situation is consistent with life experience. However, there is a difference. The result is based on the calculation from the model, while experience is just a kind of intuition.3.4.3 Second Test for the Decision of buying gasolineSince the data in 2012 is historical data now, we use artificial calculation to get the optimal value of buying gasoline. The minimum fee of driving 100 miles per week is 635.7440 USD. The result of calculating the model is 637.44 USD. The minimum fee of driving 200 miles per week is 1253.5 USD. The result of calculating the model is 1283.5 USD. The values we calculate is close to the result of the model we build. It means our model prediction effect is good. (we mention the decision people made every week and the gas price in the future is unknown. We can only predict. It’s normal to have deviation. The buying-gasoline fee which is based on predicted calculation must be higher than the minimum buying-gasoline fee which is calculated when all the gas price data are known.)We use MATLAB again to calculate the total buying-gasoline fee when n=4 and n=5. When n=4,the total fee of driving 100 miles per week is 639.4560 USD and the total fee of driving 200 miles per week is 1285 USD. When n=5, the total fee of driving 100 miles per week is 639.5840 USD and the total fee of driving 200 miles per week is 1285.9 USD. The total fee are all higher the fee when n=3. It means it is best for us to take the average prediction model of 3 phases.4. The upper bound will change the Decision of buying gasoline.Assume the consumer has a mileage driven of x1miles per week. Then, we can use 200to indicate the period of consumption, for half of a tank can supply 200-mile x1driving. Here are two situations:<1.5①200x1>1.5②200x1In situation①, the consumer is more likely to apply the decision of 200-mile consumer’s; otherwise, it is wiser to adopt the decision of 100-mile consumer’s. Therefore, x1is a critical value that changes the decision if200=1.5x1x1=133.3.Thus, the mileage driven of 133.3 miles per week changes the buying decision.Then, we consider the full-tank buyers likewise. The 100-mile consumer buys half a tank once in four weeks; the 200-mile consumer buys half a tank once in two weeks. The midpoint of buying period is 3 weeks.Assume the consumer has a mileage driven of x2miles per week. Then, we can to illustrate the buying period, since a full tank contains 400 gallons. There use 400x2are still two situations:<3③400x2>3④400x2In situation③, the consumer needs the decision of 200-mile consumer’s to prevent the gasoline from running out; in the latter situation, it is wiser to tend to the decision of 100-mile consumer’s. Therefore, x2is a critical value that changes the decision if400=3x2x2=133.3We can find that x2=x1=133.3.To wrap up, there exists an upper bound on “mileage driven”, that 133.3 miles per week is the value to switch the decision for buying weekly gasoline. The following picture simplifies the process.Chart 45. An analysis of New Y ork City5.1 The main factors that will affect the gasoline price in New York CityBased on the models above, we decide to estimate the price of gasoline according to the data collected and real circumstances in several cities. Specifically, we choose New York City as a representative one.New York City stands in the North East in the United States, with the largest population throughout the country as 8.2 million. The total area of New York City is around 1300 km2, with the land area as 785.6 km2(303.3 mi2). One of the largest trading centers in the world, New York City has a high level of resident’s consumption. As a result, the level of the price of gasoline in New York City is higher than the average regular oil price of the United States. The price level of gasoline and its fluctuation are the main factors of buying decision.Another reasonable factor we expect is the distribution of gas stations. According to the latest report, there are approximately 1670 gas stations in the city area (However, after the impact of hurricane Sandy, about 90 gas stations have been temporarily out of use because of the devastation of Sandy, and there is still around 1580 stations remaining). From the information above, we can calculate the density of gas stations thatD(gasoline station)= t e amount of gas stationstotal land area =1670 stations303.3 mi2=5.506 stations per mi2This is a respectively high value compared with several other cities the United States. It also indicates that the average distance between gas stations is relatively small. The fact that we can neglect the distance for the cars to get to the station highlights the role of the fluctuation of the price of gasoline in New York City.Also, there are approximately 1.8 million residents of New York City hold the driving license. Because the exact amount of cars in New York City is hard to determine, we choose to analyze the distribution of possible consumers. Thus, we can directly estimate the density of consumers in New York City in a similar way as that of gas stations:D(gasoline consumers)= t e amount of consumerstotal land area = 1.8 million consumers303.3 mi2=5817consumers per mi2Chart 5In addition, we expect that the fluctuation of the price of crude oil plays a critical role of the buying decision. The media in New York City is well developed, so it is convenient for citizens to look for the data of the instant price of crude oil, then to estimate the price of gasoline for the coming week if the result of our model conforms to the assumption. We will include all of these considerations in our modification of the model, which we will discuss in the next few steps.For the analysis of New York City, we apply two different models to estimate the price and help consumers make the decision.5.2 Test Models with New York dataAmong the cities in US, we pick up New York as an typical example. The gas price data is downloaded from the website () and is used in the model described in Section 2 and 3.The gas price curves between the observed data and prediction data are compared in next Figure.Figure 6The gas price between the observed data and predicted data of New York is very similar to Figure 3 in US case.Since there is little difference between the National case and New York case, the purchase strategy is same. Following the same procedure, we can compare the gas cost between the historical result and predicted result.For the case of 100 miles per week, the total cost of observed data from Feb to Oct of 2012 in New York is 636.26USD, while the total cost of predicted data in the same period is 638.78USD, which is very close. It proves that our prediction model is good. For the case of 200 miles per week, the total cost of observed data from Feb to Oct of 2012 in New York is 1271.2USD, while the total cost of predicted data in the same period is 1277.6USD, which is very close. It proves that our prediction model is good also.5.3 The analysis of resultBy comparing, though density of gas stations and density of consumers of New York is a little higher than other places but it can’t lower the total buying-gas fee. Inanother words, density of gas stations and density of consumers are not the actual factors of affecting buying-gas fee.On the other hand, we find the gas fee in New York is a bit higher than the average fee in US. We can only analyze preliminary it is because of the higher goods price in New York. We need to add price factor into prediction model. We can’t improve deeper because of the limited time. The average CPI table of New York City and USA is below:Datas Statistics website(/xg_shells/ro2xg01.htm)6. Summery& Advantage and disadvantageTo reach the solution, we make graphs of crude oil and gasoline respectively and find the similarity between them. Since the conditions are limited that consumers can only drive 100miles per week or 200miles per week, we separate the problem into two parts according to the limitation. we use Time series analysis Method to predict the gasoline price of a future period by the data of several periods in the past. Then we take the influence of international events, economic events and weather changes and so on into consideration by adding a parameter. We give each factor a weight consequently and find the rules of the solution of 100miles per week and 200miles per week. Then we discuss the upper bound and clarify the definition of upper bound to solve the problem.According to comparison from many different aspects, we confirm that the model expressed byEquation ⑨is the best. On the basis of historical data and the decision model of buying gasoline(Equation ⑦and Equation ⑧), we calculate that the actual least cost of buying gasoline is 635.7440 USD if the consumer drives 100 miles per week (the result of our model is 637.24 USD) and the actual least cost of buying gasoline is 1253.5 USD(the result of our model is 1283.5 USD) if the consumer drives 100 miles per week. The result we predicted is similar to the actual result so the predictive validity of our model is finer.Disadvantages:1.The events which we predicted are difficult to quantize accurately. The turningpoint is difficult for us to predict accurately as well.2.We only choose two kinds of train of thought to develop models so we cannotevaluate other methods that we did not discuss in this paper. Other models which are built up by other train of thought are possible to be the optimal solution.。
美国数学建模比赛历年试题Word 文档
2003 MCM ProblemsPROBLEM A: The Stunt PersonAn exciting action scene in a movie is going to be filmed, and you are the stunt coordinator! A stunt person on a motorcycle will jump over an elephant and land in a pile of cardboard boxes to cushion their fall. You need to protect the stunt person, and also use relatively few cardboard boxes (lower cost, not seen by camera, etc.).Your job is to:•determine what size boxes to use•determine how many boxes to use•determine how the boxes will be stacked•determine if any modifications to the boxes would help•generalize to different combined weights (stunt person & motorcycle) and different jump heightsNote that, in "Tomorrow Never Dies", the James Bond character on a motorcycle jumps over a helicopter.PROBLEM B: Gamma Knife Treatment PlanningStereotactic radiosurgery delivers a single high dose of ionizing radiation to a radiographically well-defined, small intracranial 3D brain tumor without delivering any significant fraction of the prescribed dose to the surrounding brain tissue. Three modalities are commonly used in this area; they are the gamma knife unit, heavy charged particle beams, and external high-energy photon beams from linear accelerators.The gamma knife unit delivers a single high dose of ionizing radiation emanating from 201 cobalt-60 unit sources through a heavy helmet. All 201 beams simultaneously intersect at the isocenter, resulting in a spherical (approximately) dose distribution at the effective dose levels. Irradiating the isocenter to deliver dose is termed a “shot.” Shots can be represented as different spheres. Four interchangeable outer collimator helmets with beam channel diameters of 4, 8, 14, and 18 mm are available for irradiating different size volumes. For a target volume larger than one shot, multiple shots can be used to cover the entire target. In practice, most target volumes are treated with 1 to 15 shots. The target volume is a bounded, three-dimensional digital image that usually consists of millions of points.The goal of radiosurgery is to deplete tumor cells while preserving normal structures.Since there are physical limitations and biological uncertainties involved in this therapy process, a treatment plan needs to account for all those limitations and uncertainties. In general, an optimal treatment plan is designed to meet the following requirements.1.Minimize the dose gradient across the target volume.2.Match specified isodose contours to the target volumes.3.Match specified dose-volume constraints of the target and critical organ.4.Minimize the integral dose to the entire volume of normal tissues or organs.5.Constrain dose to specified normal tissue points below tolerance doses.6.Minimize the maximum dose to critical volumes.In gamma unit treatment planning, we have the following constraints:1.Prohibit shots from protruding outside the target.2.Prohibit shots from overlapping (to avoid hot spots).3.Cover the target volume with effective dosage as much as possible. But at least90% of the target volume must be covered by shots.e as few shots as possible.Your tasks are to formulate the optimal treatment planning for a gamma knife unit as a sphere-packing problem, and propose an algorithm to find a solution. While designing your algorithm, you must keep in mind that your algorithm must be reasonably efficient.2002 Contest ProblemsProblem AAuthors: Tjalling YpmaTitle: Wind and WatersprayAn ornamental fountain in a large open plaza surrounded by buildings squirts water high into the air. On gusty days, the wind blows spray from the fountain onto passersby. The water-flow from the fountain is controlled by a mechanism linked to an anemometer (which measures wind speed and direction) located on top of an adjacent building. The objective of this control is to provide passersby with an acceptable balance between an attractive spectacle and a soaking: The harder the wind blows, the lower the water volume and height to which the water is squirted, hence the less spray falls outside the pool area.Your task is to devise an algorithm which uses data provided by the anemometer to adjust the water-flow from the fountain as the wind conditions change.Problem BAuthors: Bill Fox and Rich WestTitle: Airline OverbookingYou're all packed and ready to go on a trip to visit your best friend in New York City. After you check in at the ticket counter, the airline clerk announces that your flight has been overbooked. Passengers need to check in immediately to determine if they still have a seat.Historically, airlines know that only a certain percentage of passengers who have made reservations on a particular flight will actually take that flight. Consequently, most airlines overbook-that is, they take more reservations than the capacity of the aircraft. Occasionally, more passengers will want to take a flight than the capacity of the plane leading to one or more passengers being bumped and thus unable to take the flight for which they had reservations.Airlines deal with bumped passengers in various ways. Some are given nothing, some are booked on later flights on other airlines, and some are given some kind of cash or airline ticket incentive.Consider the overbooking issue in light of the current situation:Less flights by airlines from point A to point BHeightened security at and around airportsPassengers' fearLoss of billions of dollars in revenue by airlines to dateBuild a mathematical model that examines the effects that different overbooking schemes have on the revenue received by an airline company in order to find an optimal overbooking strategy, i.e., the number of people by which an airline should overbook a particular flight so that the company's revenue is maximized. Insure that your model reflects the issues above, and consider alternatives for handling "bumped" passengers. Additionally, write a short memorandum to the airline's CEO summarizing your findings and analysis.MCM2000Problem A Air traffic ControlTo improve safety and reduce air traffic controller workload, the Federal Aviation Agency (FAA) is considering adding software to the air traffic control system that would automatically detect potential aircraft flight path conflicts and alert the controller. To that end, an analyst at the FAA r traffic control system that would automatically detect potential aircraft flight path conflicts and alert the controller. To that end, an analyst at the FAA has posed the following problemsRequirement A: Given two airplanes flying in space, when should the air traffic controller ld the air traffic controller consider the objects to be too close and to require intervention?Requirement B: An airspace sector is the section of three-dimensional airspace that one air traffic controller controls. Given any airspace sector, how we measure how complex it is from an air traffic workload perspective? To what extent is complexity determined by the number of we measure how complex it is from an air traffic workload perspective? To what extent is complexity determined by the number of aircraft simultaneously passing through that sector (1) at any one instant? (2) During any given interval of time? (3) During particular time of day? How does the number of potential conflicts arising during those periods affect complexity?Does the presence of additional software tools to automatically predict conflicts and alert the controller reduce or add to this complexity?In addition to the guidelines for your report, write a summary (no more than two pages) that the FAA analyst can present to Jane Garvey, the FAA Administrator, to defend your conclusionsProblem B Radio Channel AssignmentsWe seek to model the assignment of radio channels to a symmetric network of transmitter locations over a large planar area, so as to avoid interference. One basic approach is to partition the region into regular hexagons in a grid (honeycomb-style), as shown in Figure 1, where a transmitter is located at the center of each hexagon.An interval of the frequency spectrum is to be allotted for transmitter frequencies. The interval will be divided into regularly spaced channels, which we represent by integers 1, 2, 3, ... . Each transmitter will be assigned one positive integer channel. The same channel can be used at many locations, provided that interference from nearby transmitters is avoided. Our goal is to minimize the width of the interval in the frequency spectrum that is needed to assign channels subject to some constraints. This is achieved with the concept of a span. The span is the minimum, over all assignments satisfying the constraints, of the largest channel used at any location. It is not required that every channel smaller than the span be used in an assignment that attains the span.Let s be the length of a side of one of the hexagons. We concentrate on the case that there are two levels of interferenceRequirement A: There are several constraints on frequency assignments. First, no two transmitters within distance of each other can be given the same channel. Second, due to spectral spreading, transmitters within distance 2s of each other must not be given the same or adjacent channels: Their channels must differ by at least 2. Under these constraints, what can we say about the span in,Requirement B: Repeat Requirement A, assuming the grid in the example spreads arbitrarily far in all directions.Requirement C: Repeat Requirements A and B, except assume now more generally that channels for transmitters within distance differ by at least some given integer k, while those at distance at most must still differ by at least one. What can we say about the span and about efficient strategies for designing assignments, as a function of k?Requirement D: Consider generalizations of the problem, such as several levels of interference or irregular transmitter placements. What other factors may be important to consider?Requirement E: Write an article (no more than 2 pages) for the local newspaper explaining your findingsMCM2000问题A 空间交通管制为加强安全并减少空中交通指挥员的工作量，联邦航空局(FAA)考虑对空中交通管制系统添加软件，以便自动探测飞行器飞行路线可能的冲突，并提醒指挥员。
美国数学建模竞赛成绩揭晓
美国数学建模竞赛成绩揭晓近日，2021美国大先生数学建模竞赛效果揭晓。
西交利物浦大学92个队参赛共获一等奖4个、二等奖21个、三等奖67个，获奖总数再创新高。
值得一提的是，往年西交利物浦大学初次有7个队参与交叉学科建模竞赛〔ICM〕，该赛事要求参赛者具有运用数学模型处置跨学科效果的才干，同时对应用计算机处置大批量信息的才干也有着更高的要求。
最终西交利物浦大学有6支参赛队伍取得ICM类二等奖，该获奖比例在全国各高校中首屈一指。
美国大先生数学建模竞赛〔MCM〕是数学建模范围内的国际性威望赛事，由美国自然基金协会和美国数学运用协会共同主办，美国数学学会、运筹学学会、工业与运用数学学会等多家机构协办。
自1985年以来，美国大先生数学建模竞赛曾经成功举行28届，大赛每年吸引了包括哈佛大学、麻省理工学院、北京大学、清华大学等著名高校的优秀先生参与奖项角逐，2021年吸引了来自全球各高校的3000余支队伍参与。
美国大先生数学建模竞赛分普通类型〔MCM〕和交叉学科〔ICM〕两类，往年的赛事于美国东部时间2月9日早晨8点在全球经过网络准时末尾。
MCM往年共设两个标题：关于树叶分类与重量计算的A题«TheLeavesofaTree»，关于漂流时间布置与河流容量计算的B题«CampingalongtheBigLongRiver»。
ICM的赛题是C题：从网络通讯记载中找出潜在作案同谋«ModelingforCrimeBusting»。
参赛先生3人组成一队，任选其中一题，应用树立数学模型的方法处置实践效果。
大赛不只要求参赛选手具有扎实的数学、计算机和论文写作功底，同时对其学术英文表达水平等也提出了相当高的要求。
西交利物浦大学本着〝自愿参与、自行组队、不选拔、不扫除〞的原那么，积极为先生搭建参与此高水平国际赛事的平台。
〝美国数学建模竞赛为先生们提供了一个培育数学学习与运用才干的时机，相较于竞赛效果，我们更看重的是先生在竞赛进程中失掉的锻炼时机。
美国数学建模大赛经验
一般人都认为美赛比国赛要难，这种难在思维上，美赛题目往往很新颖，一时间想不出用什么模型来解。
这些题目发散性很强，需要查找大量文献来确定题目的真正意图，美赛更为注重思想对结果的要求却不是很严格，如果你能做出一个很优秀的模型，也许结果并不理想也可能获得高奖。
另外，美赛还难在它的实现，很多东西想到了，但实现起来非常困难，这需要较高的编程水平。
除了以上的差异，在实践过程中，美赛和国赛最大的区别有三点：第一点区别当然是美赛要用英文写作，而且要阅读很多英文文献。
对于文献阅读，可以安装有道词典，开启截屏取词功能，这样基本上阅读英文文献就没什么障碍了。
对于写作，有的组是写好中文再翻译，有的是直接写英文，这两种方式都可行。
对于翻译一定至少要留出8小时来，摘要可能就要修改1小时。
如果想快点翻，可以直接使用有道词典，翻出来后再修改，虽然可能不地道，但至少比较准确，这样可大量节省翻译时间。
另外word要打开纠错功能，绿线代表拼写错误，红线代表语法错误，完成论文后整体浏览时要多注意这两种线，很可能会发现疏漏之处。
我一直认为翻译不是美赛的重点，只要能把意思表达清楚就行了，不必在翻译上浪费太多时间。
第二点区别是美赛大量的用到了启发式算法，如遗传算法、模拟退火、粒子群等等。
如果说你在国赛时还认为这些算法遥不可及，那么到了美赛你就必须掌握它了。
其实我认为对于搞编程实现的队员只要弄懂一种启发式算法就好，因为启发式算法是用来解决优化问题（多数为NP问题）的，不同算法间有很大的相似性，所以只要把一种学精了，这一类的问题就都能解了。
个人认为粒子群算法还是不错滴，遗传与模拟退火有些老套了，不过选择什么还是由你个人的接受程度决定，甚至你也可以自创算法。
第三点区别是美赛论文的排版不少人会使用Latex，一款用代码编辑的排版软件，它多用在对书籍和论文的排版上，效果美观但是操作很复杂，尤其是插入图片与表格，不是一般的麻烦。
而且，学习这种软件必须是一次性全部学完不能间断（据说完整的学习时间大概是几十个小时），只学某部分是没有用的。
美国中学生数学建模竞赛获奖论文
Abstract
In this paper, we undertake the search and find problem. In two parts of searching, we use different way to design the model, but we use the same algorithm to calculate the main solution. In Part 1, we assume that the possibilities of finding the ring in different paths are different. We give weight to each path according to the possibility of finding the ring in the path. Then we simplify the question as pass as more weight as possible in limited distance. To simplify the calculating, we use Greedy algorithm and approximate optimal solution, and we define the values of the paths(according to the weights of paths) in Greedy algorithm. We calculate the possibility according to the weight of the route and to total weights of paths in the map. In Part 2, firstly, we limit the moving area of the jogger according to the information in the map. Then we use Dijkstra arithmatic to analysis the specific area of the jogger may be in. At last, we use greedy algorithm and approximate optimal solution to get the solution.
美国数学建模比赛历年试题
2003 MCM ProblemsPROBLEM A: The Stunt PersonAn exciting action scene in a movie is going to be filmed, and you are the stunt coordinator! A stunt person on a motorcycle will jump over an elephant and land in a pile of cardboard boxes to cushion their fall. You need to protect the stunt person, and also use relatively few cardboard boxes (lower cost, not seen by camera, etc.).Your job is to:•determine what size boxes to use•determine how many boxes to use•determine how the boxes will be stacked•determine if any modifications to the boxes would help•generalize to different bined weights (stunt person & motorcycle) and different jump heightsNote that, in "Tomorrow Never Dies", the James Bond character on a motorcycle jumps over a helicopter.PROBLEM B: Gamma Knife Treatment PlanningStereotactic radiosurgery delivers a single high dose of ionizing radiation to a radiographicallywell-defined, small intracranial 3D brain tumor without delivering any significant fraction of the prescribed dose to the surrounding brain tissue. Three modalities are monly used in this area; they are the gamma knife unit, heavy charged particle beams, and external high-energy photon beams from linear accelerators.The gamma knife unit delivers a single high dose of ionizing radiation emanating from 201 cobalt-60 unit sources through a heavy helmet. All 201 beams simultaneously intersect at the isocenter, resulting in a spherical (approximately) dose distribution at the effective dose levels. Irradiating the isocenter to deliver dose is termed a “shot.” Shots can be represented as different spheres. Four interchangeable outer collimator helmets with beam channel diameters of 4, 8, 14, and 18 mm are available for irradiating different size volumes. For a target volume larger than one shot, multiple shots can be used to cover the entire target. In practice, most target volumes are treated with 1 to 15 shots. The target volume is a bounded, three-dimensional digital image that usually consists of millions of points.The goal of radiosurgery is to deplete tumor cells while preserving normal structures. Since there are physical limitations and biological uncertainties involved in this therapy process, a treatment plan needs to account for all those limitations and uncertainties. In general, an optimal treatment plan is designed to meet the following requirements.1.Minimize the dose gradient across the target volume.2.Match specified isodose contours to the target volumes.3.Match specified dose-volume constraints of the target and critical organ.4.Minimize the integral dose to the entire volume of normal tissues or organs.5.Constrain dose to specified normal tissue points below tolerance doses.6.Minimize the maximum dose to critical volumes.In gamma unit treatment planning, we have the following constraints:1.Prohibit shots from protruding outside the target.2.Prohibit shots from overlapping (to avoid hot spots).3.Cover the target volume with effective dosage as much as possible. But at least 90% of thetarget volume must be covered by shots.e as few shots as possible.Your tasks are to formulate the optimal treatment planning for a gamma knife unit as a sphere-packing problem, and propose an algorithm to find a solution. While designing your algorithm, you must keep in mind that your algorithm must be reasonably efficient.2002 Contest ProblemsProblem AAuthors: Tjalling YpmaTitle: Wind and WatersprayAn ornamental fountain in a large open plaza surrounded by buildings squirts water high into the air. On gusty days, the wind blows spray from the fountain onto passersby. The water-flow from the fountain is controlled by a mechanism linked to an anemometer (which measures wind speed and direction) located on top of an adjacent building. The objective of this control is to provide passersby with an acceptable balance between an attractive spectacle and a soaking: The harder the wind blows, the lower the water volume and height to which the water is squirted, hence the less spray falls outside the pool area.Your task is to devise an algorithm which uses data provided by the anemometer to adjust the water-flow from the fountain as the wind conditions change.Problem BAuthors: Bill Fox and Rich WestTitle: Airline OverbookingYou're all packed and ready to go on a trip to visit your best friend in New York City. After you check in at the ticket counter, the airline clerk announces that your flight has been overbooked. Passengers need to check in immediately to determine if they still have a seat.Historically, airlines know that only a certain percentage of passengers who have made reservations on a particular flight will actually take that flight. Consequently, most airlines overbook-that is, they take more reservations than the capacity of the aircraft. Occasionally, more passengers will want to take a flight than the capacity of the plane leading to one or more passengers being bumped and thus unable to take the flight for which they had reservations.Airlines deal with bumped passengers in various ways. Some are given nothing, some are booked on later flights on other airlines, and some are given some kind of cash or airline ticket incentive.Consider the overbooking issue in light of the current situation:Less flights by airlines from point A to point BHeightened security at and around airportsPassengers' fearLoss of billions of dollars in revenue by airlines to dateBuild a mathematical model that examines the effects that different overbooking schemes have on the revenue received by an airline pany in order to find an optimal overbooking strategy, i.e., the number of people by which an airline should overbook a particular flight so that the pany's revenue is maximized. Insure that your model reflects the issues above, and consider alternatives for handling "bumped" passengers. Additionally, write a short memorandum to the airline's CEO summarizing your findings and analysis.MCM2000Problem A Air traffic ControlTo improve safety and reduce air traffic controller workload, the Federal Aviation Agency (FAA) is considering adding software to the air traffic control system that would automatically detect potential aircraft flight path conflicts and alert the controller. To that end, an analyst at the FAA r traffic control system that would automatically detect potential aircraft flight path conflicts and alert the controller. To that end, an analyst at the FAA has posed the following problemsRequirement A: Given two airplanes flying in space, when should the air traffic controller ld the air traffic controller consider the objects to be too close and to require intervention?Requirement B: An airspace sector is the section of three-dimensional airspace that one air traffic controller controls. Given any airspace sector, how we measure how plex it is from an air traffic workload perspective? To what extent is plexity determined by the number of we measure how plex it is from an air traffic workload perspective? To what extent is plexity determined by the number of aircraft simultaneously passing through that sector (1) at any one instant? (2) During any given interval of time? (3) During particular time of day? How does the number of potential conflicts arising during those periods affect plexity?Does the presence of additional software tools to automatically predict conflicts and alert the controller reduce or add to this plexity?In addition to the guidelines for your report, write a summary (no more than two pages) that the FAA analyst can present to Jane Garvey, the FAA Administrator, to defend your conclusionsProblem B Radio Channel AssignmentsWe seek to model the assignment of radio channels to a symmetric network of transmitter locations over a large planar area, so as to avoid interference. One basic approach is to partition the region into regular hexagons in a grid (honeyb-style), as shown in Figure 1, where a transmitter is located at the center of each hexagon.An interval of the frequency spectrum is to be allotted for transmitter frequencies. The interval will be divided into regularly spaced channels, which we represent by integers 1, 2, 3, ... . Each transmitter will be assigned one positive integer channel. The same channel can be used at many locations, provided that interference from nearby transmitters is avoided. Our goal is to minimize the width of the interval in the frequency spectrum that is needed to assign channels subject to some constraints. This is achieved with the concept of a span. The span is the minimum, over all assignments satisfying the constraints, of the largest channel used at any location. It is not required that every channel smaller than the span be used in an assignment that attains the span.Let s be the length of a side of one of the hexagons. We concentrate on the case that there are two levels of interferenceRequirement A: There are several constraints on frequency assignments. First, no two transmitters within distance of each other can be given the same channel. Second, due to spectral spreading, transmitters within distance 2s of each other must not be given the same or adjacent channels: Their channels must differ by at least 2. Under these constraints, what can we say about the span in,Requirement B: Repeat Requirement A, assuming the grid in the example spreads arbitrarily far in all directions.Requirement C: Repeat Requirements A and B, except assume now more generally that channels for transmitters within distance differ by at least some given integer k, while those at distance at most must still differ by at least one. What can we say about the span and about efficient strategies for designing assignments, as a function of k?Requirement D: Consider generalizations of the problem, such as several levels of interference or irregular transmitter placements. What other factors may be important to consider?Requirement E: Write an article (no more than 2 pages) for the local newspaper explaining your findingsMCM2000问题A 空间交通管制为加强安全并减少空中交通指挥员的工作量，联邦航空局(FAA)考虑对空中交通管制系统添加软件，以便自动探测飞行器飞行路线可能的冲突，并提醒指挥员。
历年美国数学建模(AMCM)问题
AMCM85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。
试选择一种鱼类或哺乳动物(例如北美矮种马、鹿、免、鲑鱼、带条纹的欧洲鲈鱼)以及一个你能获得适当数据的环境，并形成一个对该动物群体的捕获量的最佳方针。
AMCM85问题-B 战购物资储备的管理钴对许多工业是必不可少的(1979年仅国防需要就占了全世界钴生产量的17％)，但是钴不产生在美国。
大部分钴来自政治上不稳定的构F地区。
见图85B-1，85B-2，85B-3。
1946年制订的战略和稀有作战物资存贮法令要求钴的储存量应保证美国能渡过三年战争时期。
50年代政府按要求存贮了，并在70年代卖掉了大部分贮量，而在70年代后期决定重新贮存，贮存的指标是8540万磅，到1982年获得了贮量的一半。
试建立一个战略金属钴的储存管理数学模型。
你需要考虑诸如以下的问题；贮量应多大?应以多大的比率来获得贮量?买这些金属的合理价格应该是多少?还要求你考虑诸如以下的问题，贮量达到多大时应开始减少贮存量?应以多大的比率来减少？卖出这些金属的合理价格应该是多少?应该怎样分配(附页中有关于钴的资源、价格、需求及再循环等方面的信息)关于钴有用信息：1985年政府计划需要2500万磅钴。
进行周而复始的生产经营，从而每年可生产600万磅钴。
1980年占总消耗量70银的120万磅钴再循环了，得到了重新处理。
AMCM86问题-A 水道测量数据表86A-1给出了在以码为单位的直角坐标为X，Y的水面一点处以英尺计的水Z．水深数据是在低潮时测得的。
船的吃水深度为5英尺。
在矩形区域(75，200)×(-50，150)里的哪些地方船要避免进入。
本题是由加州海军研究生院数学系的Richard Franke提供的，可阅他的论文Scattered Data Interpolation，Math，Comput.，38(1982)，18l-200。
美国数学建模比赛规则翻译
比赛规则，注册与指导（所有的规则与指导适用于ICM和MCM比赛，不包括附加的通知与说明）每组参加比赛的队伍必须有一个该学院的指导老师进行指导。
指导教师：请仔细阅读以下说明。
你的责任是确保参赛队伍正确注册并且顺利完成所有参加比赛的各项要求。
在参赛过程中请打印一份参赛指导以作参考。
1.开始参赛前：A. 注册B. 组建队伍2. 比赛开始后A. 通过比赛网站了解比赛试题B. 选择问题C. 团队准备解决方案D. 打印出3.比赛结束前A. 通过邮件发送一份电子版的报告。
4. 比赛结束时A. 将报告压缩打包B. 邮寄包裹5.比赛结束后A. 确认你的队伍的报告接收成功B. 查看比赛结果C. 证书D. 奖励重要说明：1、COMAP对规则与政策有最终解释权，并且可以根据自己的判断取消没有按照比赛规程和要求的队伍的注册资格。
2、如果参赛队伍被发现违规，那么该队的指导教师将被取消一年的指导资格，并且该指导教师所在学校将被取消参加下一届比赛的资格。
3、如果同一所院校的队伍被发现违反比赛规则两次，那么这个学校将至少一年不允许参加比赛。
4、所有的时间以美国东部时间为准。
一、在比赛开始之前：A 注册所有的队伍必须在美国东部时间2011年2月10日下午两点之前完成注册。
我们建议所有队伍能够提前完成所有的注册过程，因为注册系统在截至时间后不会接受任何新的注册队伍。
COMAP在任何情况下都不会接受任何迟到的MCM/ICM注册队伍。
不会有任何的特例。
●通过网站注册队伍：网址/undergraduate/contests/mcm.a.如果你是为今年的比赛注册第一支队伍，那么点击位于屏幕左手边的Register for 2011 Contest键。
输入全部要求的信息，包括你的email地址以及联系信息。
重要提示：确保提供的邮箱地址是有效并且是你现在经常使用的，这样，如果必要的话，我们在比赛的任何时间都能与您取得联系。
b.如果你已经为今年的比赛进行过队伍注册，并且想注册第二支队伍，点击Advisor Login，然后输入与第一支队伍注册用的相同的邮箱地址和密码。
中美高中数学建模竞赛比较研究
中美高中数学建模竞赛比较研究数学建模竞赛作为一种培养学生创新能力和解决实际问题能力的竞赛活动，已经在全球范围内得到了广泛的和认可。
在中国和美国，高中数学建模竞赛发展得如火如荼，它们在赛事规模、历史、参赛人数、奖项设置等方面各有特色。
本文将对中美高中数学建模竞赛进行比较研究，分析其差异及优劣之处，并探讨未来的发展方向。
规模与历史：中美高中数学建模竞赛在规模和历史方面相差无几。
两个竞赛的参赛人数均在数千人左右，且都有着多年的举办历史。
中国高中数学建模竞赛始于1989年，而美国的高中数学建模竞赛也可追溯到上世纪90年代。
奖项设置：在奖项设置方面，两个竞赛均有个人奖和团队奖。
然而，具体奖项的设置存在一定差异。
中国高中数学建模竞赛设立了一等奖、二等奖、三等奖等多个奖项，而美国高中数学建模竞赛则设立了最佳创新奖、最佳实用奖等多个奖项，更加强调对参赛作品独特性和实用性的认可。
模型类型：中美高中数学建模竞赛所涉及的模型类型基本相同，包括优化模型、统计模型、微分方程模型等。
然而，在具体题目难度的把握上，两国存在一定差异。
中国高中数学建模竞赛更加强调基础知识的掌握和应用，而美国高中数学建模竞赛则更加注重创新思维和批判性思维的培养。
难度与创新性：从比赛内容来看，中美高中数学建模竞赛的题目难度相差不大。
然而，在创新性方面，美国高中数学建模竞赛更具有优势。
比赛题目更加开放，参赛选手需要运用独特的视角和方法解决问题，更加强调参赛作品的创新性和实用性。
在访谈过程中，中美两国的参赛选手和导师均表示，数学建模竞赛为他们提供了一个良好的平台，有助于培养学生的创新能力和团队合作精神。
然而，在比赛过程中也遇到了一些挑战。
例如，比赛时间紧张，需要选手们具备快速学习和适应的能力；另外，数学建模竞赛需要选手们具备一定的编程能力，这也给一些非计算机专业的选手带来了一定困难。
对于中美高中数学建模竞赛的差异，参赛选手和导师也表达了他们的看法。
中国的参赛选手和导师认为，中国高中数学建模竞赛更加注重基础知识的考核和应用，而美国的参赛选手和导师则认为，美国高中数学建模竞赛更加注重创新思维和批判性思维的培养。
在国际学校中开展中学数学建模教育——组织参加美国高中生数学建模竞赛的启示
所以不可能让每位教师都做“吃螃蟹的人”．第三，以 学生社团形式开展，学生积极性被大大地激发．学生 是这个社团的主人，他们想学什么，怎么学都可以由 他们来决定．他们只需要在有困难的时候向指导教 师请教一下建议，或者就某一个问题与指导教师开 展讨论．师生之间是一种“生为主、师为辅”的关系， 这更有助于学生自学能力与团队合作能力的培养． 教师从学生身上，从师生或生生的讨论中也能思考 得更多，从而积累更多的经验． 虽然确定了以学生社团的形式准备、组织 ＨｉＭＣＭ，但每次活动都做些什么，都讨论些什么， 这成了每个学期都摆在师生面前的问题．经过这两 年半时间的探索，我校积累的经验可以说从一无所 有到日渐丰富． 笔者认为既然是准备建模竞赛，那么活动内容 主要应围绕常用到的知识点或者数学模型展开．形 式可以由学生讲解教师点拨、教师重点讲解以及学 生实践教师反馈等灵活组合．对于一些相对简单的 模型，如线性回归，可以先安排学生自学材料，然后 找学生代表讲解，教师加以适当补充即可．一些较难 的专题，如Ｌｏｇｉｓｔｉｃ模型、蒙特卡洛模拟等，则由教 师根据学生的程度进行重点讲解． 除此之外，我校的建模社团是一个以老带新的 社团．由高年级参加过该比赛的学生当社长，带领准 备参加比赛的低年级学生，充当社员与老师之间的 纽带．这样一方面，有些中等难度的专题可以交给高 年级的学生负责，减轻教师的负担．另一方面，高年 级学生能够以一个真正经历过连续３６小时考验的 过来人身份，向低年级学生传授经验，更清楚、更有 预判性地告诉他们在这个比赛中有哪些重要时间节 点，在这些时间节点他们应该进展到什么程度，可能 遇到什么困难．
参考文献 ［１］中华人民共和国教育部．普通高中数学新课程标准（实 验）ＥＭ］．人民教育出版社，２００７． ［２］唐盛昌主编，马峰，刘姗，刘琴编著．高中国际课程的实 践与研究／数学卷［Ｍ］．上海教育出版社，２０１２． ［３］Ｗｉｌｌｉａｍ Ｐ．Ｆｏｘ．ＨｉＭＣＭ：Ｏｕｔｓｔａｎｄｉｎｇ Ｐａｐｅｒｓ ＥＪ］．
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美国高中数学建模竞赛（HiMCM）
美国高中数学建模竞赛（HiMCM）是美国的一个非营利机构——数学及其应用社（COMAP）主办的一项美国全国性的活动。
竞赛得到了美国国家科学基金会（NSF）、运筹和管理科学研究所（INFORMS）、美国数学协会（MAA）和美国全国数学教师委员会（NCTM）的资助。
这项竞赛是在美国大学生数学建模竞赛取得成功的背景下，借鉴了大学生数学建模竞赛的模式，结合中学生的特点进行设计的。
竞赛队最多由4名高中生组成，配备一位指导老师，在指定的时间（通常由11月第一周的星期五开始）内，由参赛队自己选定的连续36个小时完成竞赛。
赛题为来自现实世界的两个实际问题，每个参赛队任选一个。
竞赛时可以用书本、计算机、软件和网络等资源，但不能和队外的任何人（包括本队的指导老师）进行任何方式的讨论。
竞赛结束后向COMPA递交答卷，由COMPA组织专家评阅，最后评出全国杰出奖（National Outstanding)、地区杰出奖（Regional Outstanding）、优秀奖（Meritorious)、荣誉奖（Honorable Mentioned)和成功参与奖（Successful Participate).为鼓励参赛学生的积极性，每个交卷的参赛队至少可以获得成功参赛奖，而获前四名的参赛队一般占总参赛队的70%以上。