(完整)美赛一等奖经验总结,推荐文档

前言：2012年3月28号晚，我知道了美赛成绩，一等奖（Meritorious Winner），没有太多的喜悦，只是感觉释怀，一年以来的努力总算有了回报。

从国赛遗憾丢掉国奖，到美赛一等，这一路走来太多的不易，感谢我的家人、队友以及朋友的支持，没有你们，我无以为继。

这篇文章在美赛结束后就已经写好了，算是对自己建模心得体会的一个总结。

现在成绩尘埃落定，我也有足够的自信把它贴出来，希望能够帮到各位对数模感兴趣的同学。

欢迎大家批评指正，欢迎与我交流，这样我们才都能进步。

个人背景：我2010年入学，所在的学校是广东省一所普通大学，今年大二，学工商管理专业，没学过编程。

学校组织参加过几届美赛，之前唯一的一个一等奖是三年前拿到的，那一队的主力师兄凭借这一奖项去了北卡罗来纳大学教堂山分校，学运筹学。

今年再次拿到一等奖，我创了两个校记录：一是第一个在大二拿到数模美赛一等奖，二是第一个在文科专业拿数模美赛一等奖。

我的数模历程如下：2011.4 校内赛三等奖2011.8 通过选拔参加暑期国赛培训（学校之前不允许大一学生参加）2011.9 国赛广东省二等奖2011.11 电工杯三等奖2012.2 美赛一等奖（Meritorious Winner）动机：我参加数学建模的动机比较单纯，完全是出于兴趣。

我的专业是工商管理，没有学过编程，觉得没必要学。

我所感兴趣的是模型本身，它的思想，它的内涵，它的发展过程、它的适用问题等等。

我希望通过学习模型，能够更好的去理解一些现象，了解其中蕴含的数学机理。

数学模型中包含着一种简洁的哲学，深刻而迷人。

当然获得荣誉方面的动机可定也有，谁不想拿奖呢？模型：数学模型的功能大致有三种：评价、优化、预测。

几乎所有模型都是围绕这三种功能来做的。

比如，今年美赛A题树叶分类属于评价模型，B题漂流露营安排则属于优化模型。

对于不同功能的模型有不同的方法，例如评价模型方法有层次分析、模糊综合评价、熵值法等；优化模型方法有启发式算法（模拟退火、遗传算法等）、仿真方法（蒙特卡洛、元胞自动机等）；预测模型方法有灰色预测、神经网络、马尔科夫链等。

美赛赛题总结一、引言概述美赛（MCM/ICM）是一项国际性的数学建模竞赛，旨在鼓励学生运用数学建模方法解决实际问题。

参与美赛不仅能够提高学生的数学建模能力，还培养了解决现实挑战的综合素养。

本文将对美赛赛题进行总结，包括解题思路、方法应用和团队合作等方面。

二、解题思路2.1 问题分析问题理解：在解题过程中，首先确保对问题有深刻的理解，梳理问题的关键信息。

问题分类：对问题进行分类，明确问题的数学建模方向，为后续的建模提供指导。

限制条件：分析问题中的各类限制条件，为建模提供问题的边界条件。

2.2 模型建立变量定义：明确定义问题中的变量，建立数学模型的基础。

方程构建：建立数学方程，将实际问题转化为数学表达式。

模型假设：根据实际情况进行模型假设，简化问题，提高建模效率。

2.3 方法选择数学工具：选择适当的数学工具，如微积分、概率统计等，用于解决建立的数学方程。

数据处理：对问题中的实际数据进行处理，确保模型的准确性。

算法应用：根据问题的特点，选择适当的算法进行求解。

三、效果评估与未来展望3.1 结果分析模型验证：针对建立的数学模型，进行模型的验证，检验模型的准确性。

结果解释：对得到的数学结果进行解释，明确数学模型在实际问题中的意义。

灵敏性分析：进行灵敏性分析，评估模型对输入参数的敏感性。

3.2 创新点总结方法创新：总结在解题过程中采用的创新方法，突出团队的独特视角。

模型创新：强调团队在模型建立中的创新点，展示团队的独特见解。

实用性评估：对模型在实际应用中的实用性进行评估，突出团队的创新成果。

3.3 团队合作分工合作：回顾团队合作过程，总结各成员在问题分析、建模和求解中的贡献。

沟通协作：强调团队成员之间的有效沟通和紧密协作对解决问题的积极影响。

团队经验：总结团队在美赛中的经验，提出未来团队合作的改进点。

总结通过对美赛赛题的总结，团队不仅加深了对实际问题的理解，还提高了数学建模和团队合作的能力。

在未来，可以更加注重创新思维，深入挖掘问题背后的数学本质，以更高水平应对各类挑战，为实际问题提供更有效的解决方案。

首页个人主页竞赛广场校园广场学神日志谢永意个性签名：Never say Never吕静我的竞赛二本民族院校也可以拿美赛Outstanding Winner 和 SIAM Award关联竞赛： 数学建模 数学 关键字： 2014年美国大学生数学建模竞赛特等奖（Outstanding Winner ） SIAM Award2014年的美赛，我们队很荣幸地拿到了Outstanding Winner SIAM Award ，这也是我国第四所大学拿到这个单项奖，之前是华中科大、清华和浙大。

可喜的是今年的SIAM 奖都来自中国的学生，浙江大学和我们西南民族大学包揽了AB 题的SIAM 奖。

应赛氪邀请，写了这一篇类似经验的分享。

我也想借这个机会总结一下我们第一次参加美赛就拿O 奖和SIAM 奖的经历，希望能给数模爱好者们一些帮助。

团队成员：谢永意，章瑶，刘一平获奖时大二都来自西南民族大学计科学院信息与计算科学1201班本篇经验贴作者为本队刘一平一、2013年五一数学建模联赛五一数学建模联赛对我们来说是我们和数学建模的相识，我们队里只有章瑶和谢永意参加了，我有事没有参加，虽然比赛规模没有国赛美赛那么大，但是对于刚上大一的我们来说，这也是一次受益终生的经历。

我们三个都是好朋友，听他俩说那次比赛经历相当坎坷，虽然是数学系的但毕竟才上大一，对于一些数学软件还是很白菜的，他们都是在短时间内学习使用软件，论文书写，还有模型建立的，那次比赛也算是为国赛奠定基础了。

那次比赛他们两个的成绩都还是不错的，一个三等奖一个二等奖，也是那次比赛让他们对国赛又充满了渴望，毕竟三天都是神经紧绷着，这种感觉很爽的，只有经历过的人才会体会到。

而且能学到很多东西。

也是那次比赛我之后才知道原来还有SPSS 这种软件。

二、2013年国赛2012年放暑假之前我们学校就有老师在上数学建模的培训课，我那个时候还没有组队，就是每天去打酱油，记得培训结束的最后一天，老师让同学们自愿组队，我本来是想回家的，不想留在学校，但是谢永意想参加国赛，所以我们就问了几个同学，但是毕竟培训都结束了好多人都组好队了，我们真的已经不抱希望了，就在这个时候我看到了我们班的学霸章瑶，就问了一下她，结果她也是想如果没人组队就直接回家的，既然我和谢永意邀请都邀请了她那就留下来培训了。

我们的美赛故事第一次知道美赛是差不多去年的这个时候，当时觉得这比赛好像挺合我胃口，就想趁着大三带着那还残存着的激情好好干一把，也就图个经历嘛。

组队的过程挺意外的，以至于我还不清楚什么情况呢就组好了队，可能因为我们三个（我，张云翼，赵晓）本来就很熟悉。

后来想想我们三个确实是个不错的组合。

大三上期中考试之后，开始对某些专业课慢慢地厌倦了起来orz，于是开始找来美赛的论文看，也就是这个时候我才开始真正了解美赛。

我看的第一篇论文In the Zone: Novel Approaches to Airplane Boarding让我对美赛有了一个直观的概念。

读这篇文章时，我惊叹于它层层推进的模型，恰如其分的解释，清晰严密的逻辑框架以及漂亮的图表和美观的排版。

它让我了解了一个看起来棘手的问题的是如何被解决的。

这篇文章对我的影响很大，以至于我们参赛论文和这篇文章在架构上有不少相近之处。

这段时间我和晓晓云翼还联系了两位参加过美赛的学长，希望他们能给我们一些建议。

靠谱的学长们详细地解答了我们的疑惑。

我们得知了数学模型能够解决哪些问题，有哪些基本的方法，需要学习哪些知识。

我们也清楚了比赛的4天内时间应该大致怎么分配，三个人如果出现了意见不合的时候应该怎么处理等等。

之后用零零散散的时间看了几篇文章也逛了逛数学中国论坛，也就快到期末考试了。

土木系大三上的期末压力实在是大，直到1月17号才考完最后一门（2月7号就美赛了哦亲~中间还有春节哦亲~）。

嗯，你没看错，我们三个好好地“在一起准备”美赛也就在这短短的20天。

期末刚刚结束，我们明马上确了三个人的分工。

其实这事不难，作为一个优质的程序员，云翼自然负责编程实现模型。

作为一个考过GRE和TOFEL的出国党，我自然负责论文的撰写，再加上我对建模感兴趣（参加美赛的初衷嘛），我也想负责建模。

晓晓自然就负责Latex排版。

虽说看起来我们的自身条件很有优势，但是我想说，绝对没有你们想得那么有优势。

建模美赛获奖范文全文共四篇示例，供读者参考第一篇示例：近日，我校数学建模团队在全国大学生数学建模竞赛中荣获一等奖的喜讯传来，这是我校首次在该比赛中获得如此优异的成绩。

本文将从建模过程、团队合作、参赛经验等方面进行详细介绍，希望能为更多热爱数学建模的同学提供一些借鉴和参考。

让我们来了解一下比赛的背景和要求。

全国大学生数学建模竞赛是由中国工程院主办，旨在促进大学生对数学建模的兴趣和掌握数学建模的基本方法和技巧。

比赛通常会设置一些实际问题，参赛队伍需要在规定时间内通过建立数学模型、分析问题、提出解决方案等步骤来完成任务。

最终评选出的优胜队伍将获得一等奖、二等奖等不同级别的奖项。

在本次比赛中，我们团队选择了一道关于城市交通拥堵研究的题目，并从交通流理论、路网优化等角度进行建模和分析。

通过对城市交通流量、拥堵原因、路段限制等方面的研究，我们提出了一种基于智能交通系统的解决方案，有效缓解了城市交通拥堵问题。

在展示环节，我们通过图表、数据分析等方式清晰地呈现了我们的建模过程和成果，最终赢得了评委的认可。

在整个建模过程中，团队合作起着至关重要的作用。

每个成员都发挥了自己的专长和优势，在分析问题、建模求解、撰写报告等方面各司其职。

团队内部的沟通和协作非常顺畅，大家都能积极提出自己的想法和看法，达成共识后再进行实际操作。

通过团队合作，我们不仅完成了比赛的任务，也培养了团队精神和合作能力，这对我们日后的学习和工作都具有重要意义。

参加数学建模竞赛是一次非常宝贵的经历，不仅能提升自己的数学建模能力，也能锻炼自己的解决问题的能力和团队协作能力。

在比赛的过程中，我们学会了如何快速建立数学模型、如何分析和解决实际问题、如何展示自己的成果等，这些能力对我们未来的学习和工作都将大有裨益。

在未来，我们将继续努力，在数学建模领域不断学习和提升自己的能力，为更多的实际问题提供有效的数学解决方案。

我们也希望通过自己的经验和教训，为更多热爱数学建模的同学提供一些指导和帮助，共同进步，共同成长。

尊敬的领导、各位老师、亲爱的同学们：大家好！在这美好的时刻，我很荣幸能够站在这里，与大家分享我近期所获得的一项荣誉。

在此，我要衷心感谢学校、老师、同学们对我的关心与支持，是你们的鼓励和帮助，让我在学业和竞赛中取得了优异的成绩。

下面，我就此次获奖经历进行简要总结。

一、获奖背景本次获奖是在全国大学生数学建模竞赛中，我所在的团队在众多参赛队伍中脱颖而出，荣获一等奖。

这次竞赛历时三天，要求我们运用数学建模的方法解决实际问题，并撰写论文。

在此过程中，我们团队紧密合作，充分发挥了团队精神，最终取得了优异的成绩。

二、获奖过程1. 确定选题：在竞赛前期，我们团队针对竞赛题目进行了深入研究，结合团队成员的专业特长，最终确定了符合我们研究方向的题目。

2. 分工合作：在选题确定后，我们根据各自的优势进行了分工，明确了每个人的责任和任务。

3. 数据收集：为了提高模型的准确性，我们团队成员查阅了大量文献资料，收集了大量的数据，为后续建模奠定了基础。

4. 模型构建：在充分了解题目背景和需求的基础上，我们团队运用数学知识，构建了适合该问题的数学模型。

5. 模型求解：在模型构建完成后，我们运用计算机软件进行求解，并对结果进行分析和验证。

6. 论文撰写：在完成模型求解后，我们团队根据竞赛要求，撰写了论文，对整个建模过程进行了总结。

三、获奖心得1. 团队合作：在此次竞赛中，我们团队充分发挥了团队精神，每个人各司其职，共同为团队的成功付出努力。

2. 勤奋学习：为了在竞赛中取得好成绩，我们团队成员在赛前进行了充分的准备，不断学习新知识，提高自己的能力。

3. 严谨求实：在建模过程中，我们注重数据的真实性和准确性，力求为问题提供可靠的解决方案。

4. 沟通交流：在团队协作中，我们注重沟通，积极交流，确保每个人都能及时了解团队进度，为共同目标而努力。

总之，此次获奖是对我们团队努力的肯定，也是对我们自身能力的提升。

在今后的学习和工作中，我们将继续发扬团队精神，努力提高自己的综合素质，为实现更高目标而努力奋斗。

2023美赛总结2023年美赛是我参加的第一次数学建模竞赛。

在比赛之前，零基础的我，由于各种原因，没有很好的学习基本的数学模型，只是看了皮毛的一点点“连续型”建模知识，因此比赛前我十分焦虑。

不过，我想着反正这是第一次参加美赛，今年虽然已经大二了，但是也不影响大二暑假的国赛和大三上学期的美赛，所以抱着“初生牛犊不怕虎”的心态，有点紧张又有点兴奋地参加了这次比赛。

正如我先前提到的，并没有做足充分准备。

所以心态就是“管他呢，不会就是不会，写完就行”。

队友，一个负责MATLAB解模编程，另一个和我一起建模+论文写作。

大家都是第一次参加，怀着第一次参加的心态，我们都认同，只要不放弃，写完论文就是最大的成功。

当然，我们更希望会享受这次的美赛经历，通过体验这一次美赛，可以获得一些有益的经验。

第二天，我提早起来，继续思考这道题。

思路还很混乱。

我就先把这个问题拆解成很多块，把思路理清楚。

后面根据队友之间的交流，把这个思路用自己的方法修改了一下理清了。

最后，我们开始把多次梳理模型有哪些，如何建立等，最后得到了一个核心思路。

第二天下午，我去看了很多论文，找了很多相关的数据，在一些偶然之中发现了一篇论文好像很有用的样子。

接着就把阻力模型弄出来了。

第三天，我们开始解模，解模发生了比较大的问题，即数据处理问题。

经过另一位建模同学的数学公式的简化和模型修改，使得最后的数据处理问题变得更加简单。

但即使如此，解模的同学还是需要花很大力气摘掉很多数据，并获得一个稍微符合常理的曲线图。

（这已然说明了我们的模型多么脆弱！）第四天，继续得到数据进行论文写作。

数据依然在不断修改。

我们三个人分别写中文底稿，把论文流程的各个板块搞了出来。

问题就是，论文写作真的很费时间，每一个板块都要花上很长的时间来完成。

好在最后还是能够把自己建立的模型自圆其说，得到了总目标时间t 的一个估计值，是1个小时40分钟左右。

最后最崩溃的地方出现在排版，在两个多小时内，把没有翻译的部分完全翻译然后排版正确真的是很考验人的啊！！总结：1、要好好准备，下学期开始看数学建模相关书籍；2、继续保持享受考试的心态。

数学建模论文写作技巧论文自评（美赛一等奖获得者从获奖论文评述中总结的经验）论文自评Successful teams would have to combine existing models, data, and new ideas in creative and original ways.（成功的队伍会把现有的模型、数据和新的思想创造性地组合起来）Here are some of the issues that kept papers from the final rounds:（以下问题会使得论文无法进入最后一轮评审）Errors in mathematics, which quickly took them out of further consideration. （数学上的错误，使他们无法进行更深层次的思考）Including mathematics that didn’t fit the flow of the presentation. In a few cases, mathematics appears to have been inserted to make a paper look more credible or to take the place of other work that had led to a dead end. （数学方法被插入论文中是为了使论文看起来更可信或是取代某些其他的工作将会使论文被淘汰）Changing notation, sometimes even within a single section. (改变符号，有时甚至在同一个章节中)Using undefined or poorly defined symbols, or using symbols before defining them. （用没有定义过的符号，或者在定义之前使用它们） ?Incomplete expressions, either because the team made an error or because the expression did not survive the word-processor. (One of theOutstanding papers addressed in this commentary had a few incomplete, probably because they didn’t survive the word-processor, but the coherence of its model and the strength of its presentation overcame that defect.)（不完整的表述）Some models were difficult to understand; poor writing was the most common cause. Another cause was the use of inapposite mathematics. Ifthe mathematics was a result of a “drive-by” insertion, fitting it into the model could be difficult.（一些模型是很难理解,可怜的写作是最常见的原因。

美赛感想[五篇模版]第一篇：美赛感想ICM参赛感想建模是解决问题的重要环节，一个美妙地解决实际问题的好方案通常由一个贴近现实而又提炼本质的模型以及若干巧妙严谨的求解工具所构成。

由于平时所接触的内容过多地囿于自己学习与研究领域中，因此为了接触新的领域并同时训练自身学习建模能力，我们聚集了来自电子系、经管学院、交叉信息研究院的三名队员参加了2014年2月举行的美国大学生数学建模及交叉学科建模大赛。

美赛要求在4天（96个小时）内针对选定的题目完成一篇完整的科技论文，因此高效合理地利用时间至关重要。

我们在比赛前进行了一定的准备，例如熟悉比赛的基本形式，准备好科技论文的LaTeX模板，并且借助清华大学数学系赛前分发的往届赛事材料对题目风格进行初步了解，并思考优秀作品所共有的特点。

通过这些准备，我们认准了比赛时的一些注意事项，包括：无论题目如何提问，整篇论文都需要有一条明确的主线；论文逻辑必须清晰，排版合理，符号与语法的使用需要符合科技论文规范；一定要完成题目里所有要求完成的部分；等等。

最重要的一点是，我们意识到若想获得好成绩，或者要有明显的创新亮点，或者要循规蹈矩地在每一点上都做得无懈可击，这也正是我们所设想的不同方向。

同时我们也确定了基本分工，即让编程能力强的交叉信息研究院队员邝仲弘负责主编程工作，让批判性思维比较好、数据收集能力强的经管学院队员孙映宁负责头脑风暴与数据收集，而让以前有科技论文撰写经验、同时在各学科上都有一定基础的电子系队员韩衍隽作为队长负责整体流程的安排与文书工作。

今年比赛于中国时间上午九点开始。

根据以往的建议，我们第一天完全仅用于确定选题。

对题目进行翻译和初步讨论后，我们首先放弃对中国学生没有优势的B题，然后分工对A题和C题进行初始建模。

由于队员邝仲弘学过有关网络科学的课程，因此我们最终选定思路更清晰也更有把握的C题，同时学习邝仲弘所共享的网络科学教材。

第二天的主要任务则是主体建模，确定整体框架，明确大部分问题的基本解答思路，完成数据搜集、编程等工作。

参加大学生建模竞赛需要具备哪些方面的能力？有哪些实战经验值得借鉴？获2019年美国大学生数学建模竞赛（MCM）与交叉学科建模竞赛（ICM）特等奖的西交利物浦大学学生团队在经验分享中总结出以下获奖心得：一、以“解决问题”为导向来思考问题，对题目首先要理解到位，而不是急于套用数学模型；二、学生须具备西交利物浦大学所倡导的“学会学习”的能力，尤其是在短时间内快速自学新知识的能力；三、组队的三位同学须在专业能力上优势互补，分工明确、紧密合作。

以下是来自西交利物浦大学获奖团队的经验分享全文：2019年美国大学生数学建模竞赛（MCM）与交叉学科建模竞赛（ICM）成绩公布，西交利物浦大学团队在全球一万多支ICM参赛队伍中脱颖而出，喜获特等奖。

今年全球仅有19支队伍获此殊荣，这也是西交利物浦大学历年参赛所获的最高荣誉。

MCM/ICM（简称“美赛”）由美国数学及其应用联合会主办，是世界范围内极具影响力的数学建模比赛。

今年组委会大幅削减了获奖率，获ICM特等奖（Outstanding Winners）的队伍仅有19支，除西交利物浦大学外，其他获奖团队来自美国杜克大学、中国人民大学、南京大学等。

摘获特等奖的西浦团队由三名大三学生——计算机科学与软件工程系的张啸天（下图左）、数学科学系的王光宇（下图中间）和张一华（下图右）组成。

数学科学系的苏炯龙博士、刘刚博士、马飞博士、费杰博士等老师在赛前为学生提供了指导。

按照竞赛要求，三人团队需在四天时间内就指定问题完成从建立模型、求解、验证到论文撰写的全部工作。

据张啸天介绍，团队选择了关于卢浮宫人流疏散的题目，该题除了需运用到数学建模的相关知识外，还要求参赛者具备良好的运筹学功底，甚至具有一定的建筑学知识。

在团队紧密合作的基础上，三人将卢浮宫的房间抽象化，做成一个可用于数学分析的网络结构模型图，再用图论的知识和数学算法来达到疏散时间最少化；同时运用运筹学算法进行优化，并用遗传算法来改善结果。

取得好成绩经验第一，运气第二，实力第三。

注重"活"，只要有道理，有思想就会有不错的成绩，选个好题成功的机会就大的多，选题不能一味的根据自己的兴趣或能力去选，还要和全体参赛队互动下（这个开玩笑了，不大容易做到，只能是在极小的范围内做到），分析下选这个题的利弊后决定选哪个题，组队和分工让三人一组参赛一是为了培养合作精神，其实更为重要的原因是这项工作需要多人合作，因为人不是万能的，掌握知识不是全面的。

组队要根据分工而来的，三个人要具备一个数学功底深厚，理论扎实，一个擅长算法实践，另一个是写作美国赛则为MCM和ICM两种，MCM为A,B两题，ICM为C 题。

总体上看ICM的获奖率则比MCM要高出不少，所以一般来讲，选C题获奖几率则比A,B两题要高出50％了。

所选的题一定要能保证做的出来，不然连个成功参赛奖都很难保证。

看起来入手容易的不一定好做，一般到一定地方后很难深入，运筹优化的很大一部分属于这类。

而看起来无从下手的题目一旦找到突破口后那就是世外桃源了，就有很多东西可做。

文献资料查找在数学建模中文献资料的查找是十分关键，通过文献资料的阅读可以知道别人在这个方面做了多少工作了，怎么做的工作，取得了哪些进展，还存在什么问题没解决，难点在哪里，热点在哪里，哪里是关键，哪些是有价值的，哪些是无意义的等等等等......，外文期刊数据库+搜索引擎查数据库是最有效率的方法，并且查学位论文是尤其推荐的，要知道查找学位论文是最高效率得到信息的途径。

虽然学位论文很长，很吓人，没有七八十页也有个一百多页，其实看多了学位论文就知道真正有用的东西页就那么个十多页最多二十多页，直接翻到那个部分看就可以了，美赛则有语言障碍，要在有限时间内完成课题研究和论文写作，则需直接查找外文文献了，要知道中国目前的总体科学水平和国外的差距是至少5年的，这个是保守估计，实际可能是2倍以上。

所以一般国外的当前研究国内鲜有涉及，当国外搞的很成熟了，产业化了，咱们国内就有教授引进了，开始研究了，吃点人家的残羹冷炙，这样说是刻薄了点，但这种情况真的不少见。

美赛写作及参赛经验分享 Revised by Jack on December 14,2020Tittle of paperSummary/Abstract Key words:I.Introduction（引言）Organ transplantation is a preferable treatment for the most serious forms of end-stage diseases. In recent years, advances in medical science and technology have made solid organ transplantation an increasingly successful and common medical procedure, a literal ''second chance at life". Not only does it offer the best hope for complete rehabilitation, but it has also proved to be the most cost-effective of all treatment options, including dialysis. Consequently, more and more people are benefiting from organ transplants and their survival rates are steadily improving. The surgical techniques involved have been mastered for half a century and are now considered as routine. The two main sources of kidneys for transplantation are deceased-donor kidneys and live-donations from family and friends. However, unfortunately, there is a considerable shortage of donor organs, compared to demands. As a matter of fact, efficient matching and allocation of organs donated has become an exigent problem.The United Network for Organ Sharing (UNOS), as the operator of the Organ Procurement and Transplantation Network (OPTN), is responsible for transplant organ distribution in the United States. UNOS oversees the allocation of many different types of transplants, including liver, kidney, pancreas, heart, lung, and cornea.Focusing on kidney transplantation, based on UNOS Kidney Allocation Model, we develop a mathematical model for US transplant networks. First, incoming organs are matched with waiting candidates by medical institutions considering the factors as ABO blood compatibility, the degree of recipient major HLA mismatch in order to obtain a matching degree applied on the allocation part. After that, from the patients’perspective, on the basis of linear regression, priority weight is defined by pondering age, disease severity, time on waiting, PRA level, and region. Applying this mechanism of ranking, we realize MWBM (Maximum Weight Bipartite-graph Matching) and SMGS (Stable Matching based on Gale-Shapley algorithm). MWBMfocuses on the optimal assignment of donors following the algorithm of bipartite-graph maximum weight matching; SMGS emphasizes the process of exchanges in order to obtain the stable exchanges between donors and candidates on the waiting list.II.The Description of Problem（问题重述)III.Basic Assumptions●The level of mismatch is only relative to the number of antigens.●The data and information are accurately registered according to the medicalmeasures●The data and information are refreshed in time according to the status of thepatients●No differences in the quality of the donor kidneys●The quality of the donor kidney is constantIV.Definitions and Notations●Kidney transplantation: A kidney transplant is a surgical procedure to implant a healthykidney into a patient with kidney failure.●Prioritization●MD: Matching Degree●PW: Prioritization weight●MWSM: Maximum Weight Bipartite Matching●SMGS: Stable Matching based on Gale-Shapley algorithm或V.ModelsThrough the investigation of US transplantation network, we draw a general picture of the mechanism. With reference to some resources available on the website of UNOS, a flow chart (Figure 1) is developed showing the procedure of the network.Currently, the initial waiting list is composed of patients who are waiting for a kidney or combined kidney-pancreas transplant. For the first time, the patients are requested to show the correct and scientific information to the US kidney transplant network which is needed for donor-recipient matching, the ranking of patients on the waiting list, and determining the outcome of those transplanted. The patients’waiting lists are composed of initial patients, historical patients and unsuccessful recipient after transplantation. Historical patients refer to registered patients whose status have changed and have an influence on the procedure. A patient is taken off the waiting list when a graft is offered and accepted by that patient or the patient is dead while waiting for a transplant. Unsuccessful recipients refer to the patients who have a bad result of transplantation calling for transplantation again, as it is so-called relistFigure 1. A schematic depicting the steps occurring in the transplantation networks......Table rate involving HLA mismatchVI.Conclusions.Our model for the optimal allocation of the donor organs is established by three modules, procurement of MD and PW, optimal assignment by MWBM model and Stable Matching of Gale-Shapley algorithm. The model has offered a convincing procedure of the allocation with the ……VII.Strengths and weaknesses(模型优缺点)Strengths●……Weaknesses●VIII.References注意文献的积累，不要等到文章写完再去重新寻找文献。

建模美赛获奖范文标题：《探索与创新：建模美赛获奖作品范文解析》建模美赛（MCM/ICM）是全球大学生数学建模竞赛的盛事，每年都吸引了众多优秀的学生参与。

在这个舞台上，获奖作品往往展现了卓越的数学建模能力、创新思维和问题解决技巧。

本文将解析一份获奖范文，带您领略建模美赛获奖作品的风采。

一、背景与问题阐述（此处详细描述范文所针对的问题背景、研究目的和意义，以及问题的具体阐述。

）二、模型建立与假设1.模型分类与选择：根据问题特点，范文选择了适当的模型进行研究和分析。

2.假设条件：明确列出建模过程中所做的主要假设，并解释其合理性。

三、模型求解与结果分析1.数据收集与处理：介绍范文中所用数据来源、处理方法及有效性验证。

2.模型求解：详细阐述模型的求解过程，包括算法选择、计算步骤等。

3.结果分析：对求解结果进行详细分析，包括图表展示、敏感性分析等。

四、模型优化与拓展1.模型优化：针对原模型存在的问题，范文提出了相应的优化方案。

2.拓展研究：对模型进行拓展，探讨其在其他领域的应用和推广价值。

五、结论与建议1.结论总结：概括范文的研究成果，强调其创新点和贡献。

2.实践意义：分析建模结果在实际问题中的应用价值和意义。

3.建议：针对问题解决，提出具体的建议和措施。

六、获奖亮点与启示1.创新思维：范文在模型选择、求解方法等方面展现出创新性。

2.严谨论证：文章结构清晰，逻辑严密，数据充分，论证有力。

3.团队合作：建模美赛强调团队协作，范文体现了成员间的紧密配合和分工合作。

总结：通过分析这份建模美赛获奖范文，我们可以学到如何从问题背景出发，建立合理的模型，进行严谨的求解和分析，以及如何优化和拓展模型。

同时，也要注重创新思维和团队合作，才能在建模美赛中脱颖而出。

Best all time college coachAbstractIn order to select the “best all time college coach” in the last century fairly, We take selecting the best male basketball coach as an example, and establish the TOPSIS sort - Comprehensive Evaluation improved model based on entropy and Analytical Hierarchy Process.The model mainly analyzed such indicators as winning rate, coaching time, the time of winning the championship, the number of races and the ability to perceive .Firstly ，Analytical Hierarchy Process and Entropy are integratively utilized to determine the index weights of the selecting indicators Secondly，Standardized matrix and parameter matrix are combined to construct the weighted standardized decision matrix. Finally, we can get the college men's basketball composite score, namely the order of male basketball coaches, which is shown in Table 7.Adolph Rupp and Mark Few are the last century and this century's "best all time college coach" respectively. It is realistic. The rank of college coaches can be clearly determined through this methods.Next, ANOVA shows that the scores of last century’s coaches and this century’s coaches have significant difference, which demonstrates that time line horizon exerts influence upon the evaluation and gender factor has no significant influence on coaches’ score. The assessment model, therefore, can be applied to both male and female coaches. Nevertheless, based on this, we have drawn coaches’ coaching ability distributing diagram under ideal situation and non-ideal situation according to the data we have found, through which we get that if time line horizon is chosen reasonably, it will not affect the selecting results. In this problem, the time line horizon of the year 2000 will not influence the selecting results.Furthermore, we put the data of the three types of sports, which have been found by us, into the above Model, and get the top 5 coaches of the three sports, which are illustrated in Table10, Table 11, Table12 and Table13 respectively. These results are compared with the results on the Internet[7], so as to examine the reasonableness of our results. We choose the sports randomly which undoubtedly shows that our model can be applied in general across both genders and all possible sports. At the same time, it also shows the practicality and effectiveness of our model.Finally, we have prepared a 1-2 page article for Sports Illustrated that explains our results and includes a non-technical explanation of our mathematical model that sports fans will understand.Key words: TOPSIS Improved Model; Entropy; Analytical Hierarchy Process;Comprehensive Evaluation Model; ANOV AContents Abstract (1)Contents (2)I. Introduction (3)П. The Basic Assumption (4)Ⅲ. Nomenclature (5)Ⅳ. Model (5)4.1 Data Processing (5)4.2 Model analysis (6)4.3 Model building (6)4.3.1 Dominant index weights calculation (7)4.3.2 Hidden index weights calculation (9)4.3.3 Positive and negative ideal solution building (12)4.3.4 Distance calculation (12)4.3.5 Comprehensive evaluation value (13)4.4 Model solution (13)4.4.1 Dominant index weights calculation (13)4.4.2 Hidden factors weights calculation (14)4.4.3 Consolidated score (16)4.5 Judgment of significant differences between the last century’s andthis century’s coaching score. (16)4.5.1 Preliminary investigation of the last century and the coach ofthe century standards (16)4.5.2 Further exploration on the influence of different time linehorizons on the assessment results (18)4.6 Test of model’s applicability to both gender (19)4.7 The selection for the top five college coaches of three sports (20)V. Analysis of our Model (22)5.1 Applications of our models (22)5.2 Strengths (22)5.3 Weaknesses (22)5.4 Future Improvements (22)Ⅵ. Conclusions (23)Ⅶ.A letter to the sports enthusiasts (23)Ⅷ. References (24)I. IntroductionTh e paper is to help "Sports Illustrated" to find the “best all time college coach” male or female.We tackle five main problems:●Build a mathematical model to choose the best college coach or coaches (past orpresent) from among either male or female coaches in such sports as college hockey or field hockey, football, baseball or softball, basketball, or soccer, and clearly articulate our metrics for assessment.●Does it make a difference which time line horizon that you use in your analysis,i.e., does coaching in 1913 differ from coaching in 2013?●Present our model’s top 5 coaches in each of 3 different sports.●Discuss how our model can be applied in general across both genders and allpossible sports.●In addition to the MCM format and requirements, prepare a 1-2 page article forSports Illustrated that explains our results and includes a non-technical explanation of our mathematical model that sports fans will understand.To tackle the first problem, we searched the indicators of Top 600 men’s basketball coaches of the American colleges. Take selecting the best male basketball coach as an example: for the explicit factors that affect assessment standards, we calculate each indicator’s weight by using Entropy method; for those implicit factors, we calculate the weight through experts’evaluation. The determination of each indicator’s score should be given by experts evaluation of each indicator. These indicators are then numericalized, and the importance of each indicator is determined through weight coefficients. Then through the multiplication of the scores of coaches’different ability indicator with corresponding weight coefficients, we get the corresponding scores, and the highest score indicates the best choice.For the second question, we first use ANOV A to determine whether significant difference exists between the scores of coaches in the last century and this century and the gender factor Significance difference shows that the time line horizon, the gender factor has influence on the assessment, whereas insignificant difference shows no influence. And based on this, we have drawn coaches’coaching ability distributing diagram under ideal situation and non-ideal situation according to the data we have found, which help us further research the influence of time line horizon on the assessment.For question 3 and 4, we put the data of the three types of sports, which have been found by us, into the Model , and get the top 5 coaches of the three sports, which are illustrated in Table10, Table 11, Table 12 and Table 13 respectively. These results are compared with the results on the Internet, so as to examine the reasonableness of our results. We choose the sports randomly, which undoubtedly shows that our model can be applied in general across both genders and all possible sports. At the same time, it also shows the practicality and effectiveness of our model.Figure1. The source of the best college coachesП. The Basic Assumption●Experts recessive factors evaluation criteria evaluation is fair and equitable.●Coaches’coaching level will increase with increasing age, but it will decline due to mental declination and the lack of the physical strength.●Assessment experts are fully known on college coaches.●The evaluation criteria only consider the factors enumerated in this paper, without considering other factors.●The evaluation criteria apply equally to men and women coaches.●We used the general data from a reliable website,Website (see Appendix).Ⅲ. NomenclatureXIndex data normalization matrix j w j Index weightsij θ Transformed normalized matrixθ+ "Positive ideal solution"θ- "Negative ideal solution"i φi comprehensive evaluation index values of being evaluated j ej Index entropy j ψ j Index Information utilityFF statisticⅣ. Model4.1 Data ProcessingIn order to better assess the extent of outstanding coaches, we selected a number of indicators to determine the coach for the "best all time college sports coach". We found information on the various indicators of data on the site and get some reliable indicators data of these college coaches. Due to the dimensions of each index inconsistencies exist, so we transformed the data to eliminate the effects of dimensionless. And through poor conversion get a normalized matrix ij m n X x ⨯⎡⎤=⎣⎦ ,1111n X m mn x x x x =, 1,2,;1,2,i m j n == ()41-ij r x =1,2,;1,2,i m j n == ()42-ij x is a dimensionless quantity and ij x []0,1∈, 1,2,;1,2,i m j n ==.4.2 Model analysisIn order to address the problems mentioned above and provide a valid, feasible assessment strategy for Sports Illustrated, we decide to select softball, basketball and football by reviewing the relevant literature. Coaching time, Competition winning rate, Cultural qualities, Athletic ability, Social skills, Ability to withstand, Innovation capacity, Ability to perceive, and so on, which are evaluation indexes. These evaluation indexes are divided into dominant factors and recessive factors. Specific factors of affecting the evaluation criteria are shown in Figure X. These indicators will be quantified and determine the degree of importance of each index by weight coefficient. When selecting coaches, the scores of the indicators multiply corresponding weight coefficient, getting corresponding scores, and the person with the highest score is the best candidate.Multi-level analysis method to determine the weight is more subjective. It is suitable to determine the weights for hidden factors, which are not used widely in both sexes and all possible requirements for sport. We need to build a more reasonable model to determine the weight for the dominant factor and recessive factors. Finally, we determine the “best all time college coach”.4.3 Model buildingWe look for the “best all time college coach” by establishing a mathematical model in Technique for Order Preference by Similarity to Ideal Solution. Take choosing the best college coach or coaches from among male coaches in such sports as basketball as an example. For the dominant factor, we calculate the weight of each indicator in Entropy Method; For the hidden factors, we calculate the weight of each indicator in expert assessment method. According to the situation of the coaches , the scores of all levels should be determined by experts, and these indicators should be quantified. Weighting coefficients represent the importance of each indicator. The scores of the indicators multiply corresponding weight coefficient to obtain the total score, and the person of highest score is the best candidate. This method is more objective, comprehensive, accurate and wide-applicable than the previous evaluation model.Flow chart of looking for the “best all time college coach” is shown in Figure 2.Figure 2.Flow chart of ModelTOPSIS Model (Technique for Order Preference by Similarity to an Ideal Solution ) was firstly introduced by C.L.Hwang and K.Yoon in 1981.TOPSIS Model is based on the proximity of a limited number of evaluation objects and idealistic goals and evaluate the relative merits of existing objects.Meanwhile, TOPSIS Model is an approximation of the ideal solution in order model, the model requires only a monotonically increasing (or decreasing) of each Utility function.Furthermore, TOPSIS multi-objective decision analysis model is a commonly used and effective model, also known as the merits of the solution from the law. The basic principle is evaluated by detecting the distance the optimal solution and the solution of the worst sort, if the evaluation of the optimal solution while the object closest to farthest from the worst solution, the result is optimal; otherwise, is not optimal，ｗhere the value of each index has reached the optimal solution for the optimal value of each index. Each index value solution has reached the worst the worst value of each index."Positive ideal solution" and "negative ideal solution" are two basic concepts TOPSIS Model. "Positive ideal solution" is an envisaged optimal solution (program), it's the individual attribute values to achieve the best value of each option; rather negative ideal solution is a solution envisaged for the worst (program ), each of which have reached the attribute value of each option in the worst value. Program to sort the various alternative rules are the ideal solution and the negative ideal solution for comparison, if one has a solution closest to the ideal solution, while away from the negative ideal solution, the solution is the best alternative solution.4.3.1 Dominant index weights calculationFor the dominant factor, we calculate the entropy method using the weight of each indicator.According to the data we found, we list the dominant influence coaches criteria indicators (see Figure 3). These dominant indicators are intuitive and easy to quantify,due to the weight of these data to calculate the specific rights-based approach, with strong objectivity. Degree of dispersion of data can be seen as the degree of disorder (entropy), the greater the entropy index data, the smaller the proportion of the index.Figure 3. Th e diagram of the entropy and weightsInformation entropy method is a method completely dependent on the data, but it is not affected by subjective factors.Figure 4. The structure of influence the selection criteria for the dominant factorsFormula to calculate the information entropy index for item j :11ln ln j ij mij i e x x m ==-∑，ij x []0,1∈ ()43- Information utility depends on the difference between the value of an index of the index information entropy between A and 1. It directly affects the size of the weight : the greater the utility value of the information , the greater the importance of the evaluation, and the greater the weight .1j je ψ=- ()43- Estimating the weight of each index using entropy method, its essence is to use the value of the coefficient to calculate the index information, the higher the value ofthe coefficient, the greater the importance of the evaluation (or the greater the weight, the bigger contribution to the evaluation results).Right item j index weight is:1j j jmi w ψψ==∑ ()43- 4.3.2 Hidden index weights calculationFor recessive factors, we take expert assessment method to calculate weights. The determination of index score at all levels should be carried out by an expert score for each indicator according to the situation of the coaches.According to the data we found, we cited the impact coach implicit criteria indicators (see Figure 4). These indicators are visually hidden but not easy to quantify. Because of these hidden right index weight calculation method based on highly subjective, we used AHP to accurately calculate the weights of these hidden indicators.AHP is a decision problem in terms of total goals layers of sub-goals, evaluation criteria and specific equipment investment program in order to break down the different hierarchies, then use judgment matrix eigenvector method to obtain the elements of each level of priority on a certain level of heavy elements, and finally re-weighted and hierarchical approach to merge the various alternative solutions to the overall goal of the final weights. "Priorities" is a relative measure, which indicates the alternative criteria for the evaluation of a program or sub-features of the target, which means excellent measure of the relative degree of each sub-target and the target level for the purposes of the relative importance of measure. Specific usage is to judge the matrix, find the maximum eigenvalue, then the corresponding feature vector normalization, finally we can get a level indicator on one level for a related indicators relative importance weights.Features of AHP are based on the nature of complex decision problems, influencing factors and internal factors affecting the relationship between in-depth analysis, and use less quantitative information to make decisions mathematical thinking process, so as to multi-target, multi-standard or non-structural properties of the complex issues simple decision making methods. Especially suitable the occasion for decision-making results difficult to directly and accurately measure.Figure 5. The structure of influence the selection criteria for the hidden factors We can know from the Figure 5 , the hierarchy is divided into one-level indicator, two-level indicators, so it belongs to the multi-level hierarchical structure model.Comparison matrix constructionAccording to the analysis of psychologists, the importance of being divided into nine grades, and secondary indicators for the level indicators can be pairwise comparison of their importance to quantify the value using the following scale.Table 1. Evaluation scaleScale Definition1 i is for j equally important3 i is for j slightly important5 i is more important for j7 i is very important for j9 i is absolutely vital for j2,4,6,8 Two intermediate value corresponding to the scaleReciprocal i compared with j,1ijijcc=or 1ijc=are obtained for the judge valueAccording to the above scale, relative matrix is as follows:By comparison, the comparison matrix of level indicators and secondary indicators are as follows:1111ni n nn c c C c c =()44-Due to the above judgment matrix symmetry, so when filling out, usually the first to fill 1ii c =section, and then judge and triangular or lower triangular() 1/2n n -elements on the form. In exceptional circumstances, the judgment matrix is transitive, that satisfies the equation ：ik kj ij c c c *= .When the formula to determine all the elements of the matrix are established, the consistency of judgment matrix is a matrix.Level single-sorting (Weight vector calculation) and TestFor the judgment of experts to fill in the matrix ，we took advantage of some mathematical methods for sorting. Level single-sorting refers to the various factors of each judgment matrix for weight relative weights of the criteria, so essentially calculating the weight vector. There are many ways to calculate the weight vector, such as the eigen value method, and the method, the root method, power method. Here is a brief overview and method.Principle "and the law", for consistency of judgment matrix, each column after normalization, we can get the corresponding weights. For non-consistency of judgment matrix, each column after normalization, which can be approximated by the corresponding weights, n column vectors and these strike the arithmetic average as the final weight of the weight. Specific formula is:111n iji nj klk c W n c ===∑∑ ()45- It should be noted that, in the layers of the sort , you need to test the consistency of judgment matrix . In exceptional circumstances , determining the matrix has passed and consistency. Under normal circumstances, the judge is not required to meet the strict nature of the matrix . But looking at the human understanding of the law , a right to judge the importance of the matrix there is some sort of logical law . For example, if A is more important than B, and B surpasses C importantly , from a logical perspective , A should be significantly more important than C, if the two a comparison of two important results than C , then the consistency of judgment matrix in violation of norms, logically unreasonable. If pairwise comparisons, C is more important than the result of A, the consistency of judgement matrix in violation of the guidelines, it was logically irrational.Therefore, in practice it is required to meet the general consistency of judgment matrix, which requires consistency checking. Only by testing can it illustrate that the logical judgment matrix is reasonable and to continue to analyze the results. Steps of consistency test are as follows.First , calculate the consistency index ..C I (consistency index).max..1nC I n λ-=- ()46- Second , look-up table to determine the corresponding average random consistency index ..R I （random index ）According to the different order of judgment matrix, we check the table below, and get the average random consistency index ..R I For example, for a 5-order judgment matrix, we can get ..R I = 1.12 easily.0 0 0.52 0.89 1.12 1.26 1.36 1.411.41.491.521.541.561.581.59Third , calculate the proportion of consistency ..C R (consistency ratio) and determine.......C I C R R I = ()47- When ..0.1C R <, the consistency of judgment matrix is considered acceptable and when .. 0.1C R >, it is considered the consistency of judgment matrix does not meet the requirements, we need to re-amend the judgment matrix. 4.3.3 Positive and negative ideal solution buildingWe define ,1,2,;1,2,;ij ij ij w x i m j n θ=•==Determine the positive idealsolution θ+and negative ideal solution θ-；Assuming positive ideal solution θ+Negative ideal solution ：{}min ,1,2,;1,2,;j ij ii m j n θθ-=== Positive ideal solution ：{}min ,1,2,;1,2,;j ij ii m j n θθ+===4.3.4 Distance calculationT he Euclidean distance between being evaluated and Positive ideal solution1,2,i d i m +==⋅⋅⋅ ()48-The Euclidean distance between being evaluated and Negative ideal solution_1,2,i d i m ==⋅⋅⋅ ()49-4.3.5 Comprehensive evaluation valueThe value of comprehensive evaluation index evaluated is,1,2,i i i id i m d d φ-+-==⋅⋅⋅+ ()410- 4.4 Model solution4.4.1 Dominant index weights calculationWe find four dominant indicators for the last century of the impact evaluation criteria through the network, namely, "The time of winning the championship", "The number of races", "Coaching time", "Completion wining rate". Specific data are in Table 3.Table 3. Four indicators for men's basketball coachesHank Iba 29 1085 40 0.693 Ray Meyer 20 1078 42 0.672 Don Haskins 29 1072 38 0.671 Adolph Rupp 71 1066 41 0.822 E.A. Diddle 17 1061 42 0.715 Ralph Miller 17 1044 38 0.646 Slats Gill 13 992 36 0.604 Norm Stewart 30 967 32 0.656 Tony Hinkle 4 952 41 0.586 Norm Sloan 14 917 33 0.609 Jack Friel 3 872 30 0.568 Guy Lewis 26 871 30 0.68 Ned Wulk 17 837 31 0.59 JohnThompson 37835 27 0.714 John Wooden 54 826 29 0.804 Bill E. Foster 7 820 30 0.515 Johnny Orr 13812 29 0.574 … …………We will enter the above data by calculated entropy method to get the dominant index weights as follows:Table 4. Men's basketball coach dominant index weights tableDominant indexThe time ofwinning thechampionshipThe number ofraceCoachingtimeCompletionwinning rateweight0.7481 0.1195 0.1145 0.0178From the Table 4 we can observe "The time of winning the championship" share of the weight is larger than the "Completion wining rate". But the proportion of "The number of race" and "Coaching time", is less. This shows that the dominant indicators, "The time of winning the championship" for the selection of the coach plays a very important role.Figure 6. Men's basketball coach dominant index weights pie From Figure6, we can observe that"The time of winning the championship" significant weightings are larger in the share of other indicators. On the surface this is actually somewhat contradictory, but in fact, as "The time of winning the championship"indicators of the degree of dispersion is larger, therefore,its impact is huge coach rankings, while the smaller degree of dispersion of other indicators,so they rank impact on the coach is smaller.4.4.2 Hidden factors weights calculationUsing the comparison scale of the model we c an go to the comparison matrix level indicators and secondary indicators. Since the pairwise comparison is subjective, the Hidden factors weight is subjective. Using the way of expert reviewing, finding information or questionnaires to get the comparison matrix. Then calculate the weights. Then we examined whether it could through consistency test.We did a series of comparison matrix and then through examination we selected the following comparison matrix.Table 5. The best comparison matrix of the University men's basketball coach indicatorsHidden factors CulturalqualitiesAthleticabilitySocialskillsAbility towithstandInnovationcapacityThe abilityto perceiveCultural qualities 1 1/3 1/3 1/3 1/6 1/7 Athletic ability 3 1 1/3 1/3 1/5 1/5Social skills 3 3 1 1/3 1/5 1/4 Ability to withstand 5 3 3 1 1/3 1/5 Innovation capacity 6 5 5 3 1 1/4 The ability toperceive7 5 4 5 4 1Known by its consistency index, ..0.98700.10C R=<，.. 0.1360C I=, so it can go through consistency test. The maximum value weight , 6.6799λ=,which we calculated are shown in Table 5.The consistency is index..0.98700.10C R=<，.. 0.1360C I=. Through consistency test, the maximum characteristic value is 6.6799λ=. Weights form table below.Table 6. Best college men's basketball coach recessive factor index weights tableHidden factors CulturalqualitiesAthleticabilitySocialskillsAbility towithstandInnovationcapacityThe abilityto perceiveWeights 0.0345 0.0554 0.0836 0.1340 0.2491 0.4435Figure 7. Best college men's basketball coach recessive factor index weights pieWe obtain the weight values though consistency test and Analytic Hierarchy Process, Athletic ability of coaches is great importance of hidden index. Second, the cultural qualities, the innovation capacity is not important. The results are subjective more or less. We can not be generalized, with the development of society, the proportion of innovative indicators may increase.4.4.3 Consolidated scoreweights above put in TOPSIS model can get a score for each coach. Because hidden indicators expert review in our paper is difficult to achieve. Thus weakened expert evaluation index, highlighting calculations dominant indicators.Thus, we get the following scoring table.Table 7. Last century University men's basketball coach total scoreDean Smith 70 1133 36 0.776 0.0181 John Wooden 54 826 29 0.804 0.0139 John Thompson 37 835 27 0.714 0.0097 Norm Stewart 30 967 32 0.656 0.0081 Hank Iba 29 1085 40 0.693 0.0080 Don Haskins 29 1072 38 0.671 0.0080 Guy Lewis 26 871 30 0.68 0.0071 Everett Case 27 511 19 0.738 0.0071 Lou Carnesecca 26 726 24 0.725 0.0070 Gene Bartow 25 744 24 0.66 0.0067 Neil McCarthy 23 681 23 0.665 0.0062 Pete Carril 22 798 30 0.658 0.0061 Frank McGuire 22 785 30 0.699 0.0061 Joe B. Hall 23 463 16 0.721 0.0060 Jack Gardner 22 721 28 0.674 0.0060 Ray Meyer 20 1078 42 0.672 0.0058 Terry Holland 21 634 21 0.659 0.0057 ………………As can be seen from Table 7Adolph Rupp's highest overall score, it is reasonable to judge him in the last century's "best all time college coach". Due to the weakening of the influence of implicit indicators, so here was "best all time college coach" on the hidden indicators may have less. Using the same method to evaluate the coach of the century can be the century of the "best all time college coach" is Mark Few.4.5 Judgment of significant differences between the last century’s and this century’s coaching score.4.5.1 Preliminary investigation of the last century and the coach of the century standards.Taking into account the tremendous changes in the last century and this century,。

参加美国大学生数学建模竞赛经验分享第一篇：参加美国大学生数学建模竞赛经验分享一、组队篇：团队水平基本决定了最终结果的上限——在美国赛，差团队是无可能做出好结果的（这点与国赛不同）无论队员还是导师，猎取的优先级都应该是这样： 1.2.3.4.没过得奖但有经验的：这种动力最足得过奖的：如果后来参赛成绩还不如之前，对人对己都交代不过去没经验但想得奖的：大多数没经验、想打酱油：不光说队员，还要留意导师，你懂的^_^ 这跟创业组队一样，别在乎现在神马光环，关键看的是将来能够付出多少。

必须保证团队里每个人都有共同的愿景和强大的动力，否则内耗是迟早的事。

高手和好导师都是稀缺资源，下手越早收获可能越大，想找高手：••你必须也是个高手，至少某方面特长能给人信心；必须保证团队间能衷诚合作，互相鼓励/配合——这点与谈恋爱一样，要经一定时间的磨合和考验，才能看得清；•保证每个人的弱点能有效弥补，即便是高手全才也不多，对其弱点如果没有合适的人弥补，结果可能还不如实力平均的队伍；•要能顶得住本校其他队的竞争，比如挖人、争导师、抢机房等等——人才太多没办法，哎～总之，除了主动出击、笨鸟先飞之外，还要求一定的口碑、人脉和组织能力、观察精准、明决善断，敢于取舍。

二、装备篇：•数值工具：各种软件和代码操弄熟练是基本要求，软件不必求多，但每款特色部分一定要尽可能熟。

长的代码尽量拆成短的，而且要调通测试过，关键地方注释好，比赛时，宝贵的时间用来debug是不值的；•信息检索：搜索引擎技巧是根本，其他信息含量都不太高，国内各种数理论坛算是基础，国外各种资源尽量积累（免费论文库、wiki、各大数值软件官网、专业论坛、大牛的blog/twitter、stackoverflow、quora……不会翻墙的要尽量先弄清楚，不然有的资源打不开或者下不到哦），图书馆的国外学术资源也别忽视；••写作软件：有时间精力的同学学一下LaTeX，实在没时间的将就用word转pdf吧；资料积累：钱少的同学可以下outstanding论文，仔细研究（新浪爱问和madio上能下到2011年前的）；钱多的可以买comap的杂志，不只为看论文，主要看每题的综述，了解那一题当年的答题情况和阅卷人的思路（我那几年国内有卖的，之后几年没关注了，不清楚现在哪能弄到）。

美赛总结报告2022年美国数学建模竞赛总结报告亲爱的评委们：首先，我代表我们的团队，向您致以最诚挚的问候和感谢。

2022年的美国数学建模竞赛对我们来说是一次难得的经历和机会，我们珍惜并充分发挥我们的最佳水平。

通过这次竞赛，我们不仅收获了许多宝贵的经验和知识，也锻炼了我们的团队合作能力和解决问题的能力。

这次竞赛的题目是关于城市扩张与环保问题的，我们团队选择了其中一道题目进行研究和建模。

在初步了解问题后，我们进行了大量的文献查阅和数据分析，以便更好地了解城市扩张对环境的影响，并找到有效的解决方案。

在研究过程中，我们运用了许多数学模型的技巧和方法，如回归分析、优化模型和概率模型等。

同时，我们也结合实际情况，考虑到了城市发展的多个方面，如人口增长、经济发展和环境保护等因素。

通过建立数学模型，我们成功地分析了城市扩张对环境的潜在影响，并给出了一些合理的建议和解决方案。

在团队合作方面，我们深刻体验到了团队合作的重要性。

每个队员都充分发挥自己的优势，积极参与讨论和建模过程，在紧张的竞赛中，我们相互支持、相互鼓励，共同解决了许多难题。

通过团队合作，我们有效地利用每个队员的才能和潜力，最大程度地提高了团队的整体水平。

此外，这次竞赛对我们的时间管理和应变能力也提出了很高的要求。

我们充分利用有限的时间，提前做好了计划和准备，确保了我们在规定的时间内能够完成建模和分析任务。

同时，在面对一些突发情况时，我们能够迅速应对和调整，保持了团队的稳定和良好的工作状态。

最后，我想再次感谢评委们对我们的认可和支持。

这次竞赛是我们在数学建模方面的一个重要的里程碑，它不仅给我们带来了宝贵的经验和机会，也激发了我们对数学建模的热情和兴趣。

我们将继续努力学习和提升自己，在未来的学习和工作中，更好地应用数学模型解决实际问题。

谢谢评委们的聆听！祝愿您们工作顺利！我们团队敬上。

XXX团队。

2023美赛建模经历总结引言我参与了2023年的美赛建模比赛，并在比赛中取得了一些经验和收获。

在本文档中，我将总结我的经验，并分享我的观点和方法。

赛前准备参加美赛建模比赛需要进行一系列的准备工作。

首先，我们需要熟悉比赛的规则和要求。

这包括了解比赛的主题、时间要求、评分标准等等。

其次，我们需要组队，并明确每个队员的职责和分工。

每个队员都应该负责自己擅长的领域，这样可以充分发挥大家的优势。

最后，我们还需要制定一个详细的计划，以确保我们按时完成各个阶段的任务。

队内协作队内协作是美赛建模比赛中至关重要的一环。

在我们的队伍中，我们通过定期的会议和讨论来促进团队合作。

每个队员都有机会分享自己的想法和观点，并提出改进的建议。

我们建立了一个有效的沟通渠道，以便及时解决问题和调整计划。

此外，我们还利用在线协作工具共享文件和资源，方便队员之间的合作和交流。

数据收集和处理在比赛中，数据收集和处理是一个重要的环节。

我们需要收集相关的数据，并进行合理的处理和分析。

对于大规模的数据集，我们采用了并行计算和分布式处理的方法，以提高效率和准确性。

此外，我们还利用数据可视化工具，将数据以图表的形式展示出来，使得我们可以更直观地理解和分析数据。

模型构建和验证在美赛建模比赛中，我们需要构建一个有效的模型，并对其进行验证。

在模型构建过程中，我们采用了多种方法和技术。

我们首先对数据进行预处理，包括数据清洗、特征提取等等。

然后，我们尝试了多个模型，并比较它们的性能。

最后，我们选择了一个最好的模型，并进行了交叉验证和参数调优，以确保模型的准确性和稳定性。

结果分析和总结在比赛结束后，我们对结果进行了分析和总结。

我们回顾了我们的方法和策略，并评估了我们的模型的性能。

我们还比较了我们的结果和其他队伍的结果，以了解我们在比赛中的竞争力。

最后，我们总结了我们的经验和教训，并提出了改进的建议。

结论通过参与2023年的美赛建模比赛，我学到了很多知识和经验。

美赛题型方法总结第1篇F题主要是政策问题，会涉及到如何制定政策，制定政策需要考虑的对象，成本，限制因素，成效等一系列相关问题。

建模方法1.层次分析法层次分析法是指将一个复杂的多目标决策问题作为一个系统，将目标分解为多个目标或准则，进而分解为多指标(或准则、约束)的若干层次，通过定性指标模糊量化方法算出层次单排序(权数)和总排序，以作为目标(多指标)、多方案优化决策的系统方法。

如果政策中存在多种因素，即可用层次分析法来分出各种因素的重要程度。

2.概率论与数理统计若制定政策的结果中涉及到政策实行后的效果分析，则会用到动态演化模型对实行后的数据变化进行监控。

可以用一些差分方程或常微分方程表示，一方面能够预测监控数据随时间的变化，另一方面能够解释一些状态。

3.博弈论博弈论主要研究公式化了的激励结构间的相互作用，是研究具有斗争或竞争性质现象的数学理论和方法。

博弈论考虑游戏中的个体的预测行为和实际行为，并研究它们的优化策略。

生物学家使用博弈理论来理解和预测进化论的某些结果。

以上就是小拓君这期分享的全部内容啦亲亲们好好准备好好加油哦小拓君等你的好消息哦~美赛题型方法总结第2篇下面，小竞带你看一看2019年E题——“环境退化的代价是什么”经济学理论往往忽视其决策对生物圈的影响，或者假设没有限制的资源或能力来满足生物圈的需要。

这种观点存在缺陷，环境现在正面临后果。

生物圈提供了许多自然过程来维持健康和可持续的人类生活环境，这就是所谓的生态系统服务。

然而，只要人类改变生态系统，我们就有可能限制或移除生态系统服务。

现在考虑一下这些项目中的许多项目在一个地区、国家和世界范围内的影响。

有没有可能对土地利用开发项目的环境成本进行估价？环境退化将如何计入这些项目成本？一旦将生态系统服务计入项目的成本效益比，就可以确定和评估该项目的真实和全面的估值。

您的ICM团队已受雇创建生态服务评估模型，以便在考虑生态系统服务时了解土地使用项目的真实经济成本。