2019年美赛D题特等奖论文
美赛论文
注：LEO 低地球轨道MEO中地球轨道GeO 同步卫星轨道risk-profit 风险利润率fixed-profit rate 固定利润率提出一个合理的商业计划，可以使我们抓住商业机会，我们建立四个模型来分析三个替代方案（水射流，激光，卫星）和组合，然后确定是否存在一个经济上有吸引力的机会，从而设计了四种模型分析空间碎片的风险、成本、利润和预测。
首先，我们建立了利润模型基于净现值（NPV）模型，并确定三个最佳组合的替代品与定性分析：1）考虑了三个备选方案的组合时，碎片的量是巨大的；2）考虑了水射流和激光的结合，认为碎片的大小不太大；3）把卫星和激光的结合当尺寸的这些碎片足够大。
其次，建立风险定性分析模型，对影响因素进行分析在每一种替代的风险，并得出一个结论，风险将逐渐下降直到达到一个稳定的数字。
在定量分析技术投入和对设备的影响投资中，我们建立了双重技术的学习曲线模型,找到成本的变化规律与时间的变化。
然后，我们开发的差分方程预测模型预测的量在未来的四年内每年发射的飞机。
结合结果我们从预测中，我们可以确定最佳的去除选择。
最后，分析了模型的灵敏度，讨论了模型的优势和我们的模型的弱点，目前的非技术性的信，指出了未来工作。
目录1，简介1.1问题的背景1.2可行方案1.3一般的假设1.4我们的思想的轮廓2，我们的模型2.1 时间---利润模型2.1.1 模型的符号2.1.2 模型建立2.1.3 结果与分析2.2 . 差分方程的预测模型2.2.1 模型建立2.2.2 结果分析2.3 双因子技术-学习曲线模型2.3.1 模型背景知识2.3.2 模型的符号2.3.3 模型建立2.3.4 结果分析2.4风险定性分析模型2.4.1 模型背景2.4.2 模型建立2.4.3 结果与分析3．在我们模型的灵敏度分析3.1 差分方程的预测模型。
3.1.1 稳定性分析3.1.2 敏感性分析3.2 双因子技术学习曲线模型3.2.1 稳定性分析3.2.2 敏感性分析4 优点和缺点查分方程预测模型优点缺点双因子技术学习曲线模型优点缺点时间---利润模型优点缺点5..结论6..未来的工作7．参考双赢模式：拯救地球，抓住机遇1..简介问题的背景空间曾经很干净整洁。
建模美赛获奖范文
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。
2019数学建模美赛论文
2019MCM/ICMSummary Sheet (Your team's summary should be included as the first page of your electronic submission.)Type a summary of your results on this page. Do not includethe name of your school, advisor , or team members on this page.Ecosystems provide many natural processes to maintain a healthy and sustainable environment after human life. However, over the past decades, rapid industrial development and other anthropogenic activities have been limiting or removing ecosystem services. It is necessary to access the impact of human activities on biodiversity and environmental degradation.The main purpose of this work is to understand the true economic costs of land use projects when ecosystem services are considered. To this end, we propose an ecological service assessment model to perform a cost benefit analysis of land use development projects of varying sites, from small-scale community projects to large national projects. We mainly focus on the treatment cost of environmental pollution in land use from three aspects: air pollution, solid waste and water pollution. We collect pollution data nationwide from 2010 to 2015 to estimate economic costs. We visually analyze the change in economic costs over time via some charts. We also analyze how the economic cost changes with time by using linear regression method. We divide the data into small community projects data (living pollution data) and large natural data (industrial pollution data). Our results indicate that the economic costs of restoring economical services for different scales of land use are different. For small-scale land, according to our analysis, the treatment cost of living pollution is about 30 million every year in China. With the rapid development of technology, the cost is lower than past years. For large-scale land, according to our analysis, the treatment cost of industrial pollution is about 8 million, which is lower than cost of living pollution. Meanwhile the cost is trending down due to technology development. The theory developed here provides a sound foundation for effective decision making policies on land use projects.Key words: economic cost , ecosystem service, ecological service assesment model, pollution.Team Control Number For office use onlyFor office use only T1 ________________F1 ________________ T2 ________________F2 ________________ T3 ________________Problem Chosen F3 ________________ T4 ________________F4 ________________EContent1. Introduction (3)2. Variable Description (3)2.1 Definitions (3)3. Model Definitions and Results (4)3.1 2010-2015 Air Pollution Virtual Governance Cost (4)3.2 2010-2015 Solid Waste Virtual Governance Cost (8)3.3 2010-2015 Water Pollution Virtual Governance Cost (11)3.4 Total Governance Data Analysis (14)3.5 Model Analysis Over Time (15)4. Conclusions (16)4.1 Strengths (17)4.2 Weakness (17)4.3 Model Improvement (17)5. References (18)1.IntroductionToday, with the rapid development of social industrialization and modernization, one thing we must admit is that in the process of industrialization in different regions, countries and even the whole world, we are trying to maximize the economic benefits of limited development space, but at the same time, the impact of decision-making on the biosphere is also ignored.It is well known that the biosphere provides many natural processes to maintain healthy and sustainable human living environments, which are called ecosystem services. It not only provides food, medicine and raw materials for industrial and agricultural production, but also maintains the life support system that human beings rely on for survival and development. At present, the internationally recognized ecosystem service function classification system is a classification method proposed by MA working group. MA's ecosystem service classification system divides the main service function types into four function groups: product, rule, culture and support. Thus, the ecosystem service function is the foundation of human civilization and sustainable development.But as we use and update our environment, we may limit or eliminate ecosystem services. Although these activities seem insignificant, they do affect the ecosystem to some extent.Therefore, we introduce the virtual cost management method, which aims to evaluate the cost of environmental degradation by calculating the cost of pollution control. This allows for a comprehensive assessment of projects under construction, thus ensuring the resilience and sustainability of ecosystem services while utilizing land resources.To ensure the integrity and the sustainable development of ecological system is the premise and foundation of development, If we simply pursue the speed of development and neglect the pressure that construction activities bring to the ecosystem, will inevitably produce the bad consequences, we must protect our earth, because it is not only our planet, but also the planet of our descendants.2.Variable Description2.1 DefinitionsTo measure the cost of environmental degradation, we introduced the following variables:Virtual governance costs: The environmental degradation value calculated by the pollution loss method is called the environmental degradation cost, which refers to the various damages caused by the pollutants discharged in the production and consumption process to the environmental function, human health and crop yield under the current governance level.Environmental loss method: In the SEEA framework, pollution loss method refers to the environmental value assessment method based on damage. This method uses certain technical means and pollution loss investigation to calculate all kinds of damages caused by environmental pollution.Biodiversity: Refers to the variety of life in an ecosystem; all of the living organisms within a given area.Biosphere: The part of the Earth that is occupied by living organisms and generally includes the interaction between these organisms and their physical environment.Ecosystem: A subset of the biosphere that primarily focuses on the interaction between living things and their physical environment.Ecosystem Services: The many benefits and assets that humans receive freely from our natural environment and a fully functioning ecosystem.Environmental Degradation: The deterioration or compromise of the natural environment through consumption of assets either by natural processes or human activities.Mitigate: To make less severe, painful, or impactful.Valuation: Refers to the estimating or determining the current worth of something.3.Model Definitions and Results3.1 2010-2015 Air Pollution Virtual Governance CostThe natural gas use proportion c1,c2 and c3 are collectively recorded as c, the gas use amount d1,d2 and d3are collectively recorded as d, and the natural gas operation cost f1,f2 and f3 are collectively recorded as f.In the modeling process, it is assumed that the garbage disposal cost, gas operation cost and central heating operation cost will not change in the next few years.Urbanization is often accompanied by land development and utilization. Urban construction is inseparable from industrial construction and improvement of human living environment. In this process, the continuous development and utilization of land, or the construction of large factories, or the increase of communities and parks, has a certain impact on the local ecology. Factory waste emissions and gas consumption to a certain extent polluted the air, causing irreversible damage to the ecological environment.The impact of land development and utilization on the atmosphere is manifold. In large national projects such as industrial projects, we focus on the costs of sulphur dioxide emissions and dust emissions. In small-scale community projects such as community life, we mainly study the governance cost from the perspective of gas use, and establish an ecological service evaluation model related to air pollution.When considering the function of ecological services, we first study the economic cost of land development and utilization from the perspective of large-scale land development and utilization. Large-scale land development generally includes the establishment and relocation of large companies, the laying of pipelines nationwide, and the establishment of large state-owned enterprises. The construction of large chemical plants in land development produces polluting gases during the development process or after the plant is put into use. Among them, sulfur dioxide and dust emissions accounted for a large proportion. Let's use these two types of pollution as an example to calculate the economic cost.Collecting China's SO2 and dust emissions from 2010 to 2015:The unit cost of SO2 and dustIt should be noted that in the process of treatment, the exhaust gas can not be completely treated. The virtual removal rate of SO2 was a2=90%, and the virtual removal rate of dust was b2=97%. The cost of waste gas treatment should be calculated by virtual management method. It is that the total amount of emissions× virtual removal rate× unit governance cost.SO2 governance cost=a×a2×a1Dust governance cost=b×b2×b1The calculations above can be used to estimate the cost of governance. Governance cost=SO2 governance cost+ dust governance cost.Secondly, when considering the function of ecological services, the cost of land development and utilization is studied from the perspective of small-scale land development and utilization. The development and utilization of small-scale land generally includes the construction of roads, sewers, houses and Bridges. Let's take the development of land for residential areas as an example. When people live in communities, using natural gas for heating and living also pollutes the air. We mainly consider the impact of artificial gas, natural gas and liquefied petroleum gas.Collecting data on gas use in urban areas of China from 2010 to 2015:Gas operating costs, urban population, urban gasification rate:The cost of domestic gas treatment is related to urban population, urban gas rate, gas consumption, various proportions of gas consumption, heating area and operating cost. We use hypothetical assumptions to define the cost of gas pollution control.Gas pollution control cost=∑（p −(1−q)×c ×d ×f ）3n=1+（s 1-s 2）×eWhere n=1 is artificial gas, n=2 is natural gas, and n=3 is liquefied petroleum gas. The following estimates of governance costs can be obtained by calculation:3.2 2010-2015 Solid Waste Virtual Governance CostIn the calculation process, we assume that the unit governance cost is the same, including the cost of general solid waste management, the cost of hazardous waste management, the cost of household garbage removal, the cost of sanitary landfill, and the cost of harmless incineration.In the process of land development and utilization, both large-scale land use and small-scale land use will inevitably produce solid waste.In the process of construction and normal use, large factories mainly produce general solid waste and hazardous waste. There are two ways to treat solid waste: storage and disposal. When calculating the cost of industrial solid waste treatment, we can start with the cost of waste storage and management and the cost of waste disposal.Collecting data on China's industrial solid waste treatment from 2010 to 2015:HS means hazardous waste storage HD means hazardous waste disposal The unit cost of industrial solid waste:According to the above data, the virtual governance method is used to calculatethe cost of large-scale land development and utilization when the ecological service function is considered.Virtual governance cost of industrial solid waste=virtual waste management cost of storage waste+ virtual governance cost of disposal wasteVirtual governance cost of storage waste=e1+e2Virtual governance cost of disposal waste=f1+f2e1=a× (b1-a1)e2=c× (d1-c1)f1=b×b1f2=d×d1The virtual governance cost of industrial solid waste can be obtained by calculation:When small-scale land is used for community construction, a certain amount of household garbage will be produced. There are many ways to deal with household garbage. When calculating the treatment cost, we mainly follow three treatment methods: cleaning, sanitary landfill and innocuous incineration.Collecting the amount of domestic garbage generated by Chinese Residents from 2010 to 2015:Domestic garbage unit treatment cost:Virtual governance cost of domestic garbage=x1+y1+z1x1=m×(z-x)y1=n×(y+ z)z1=r× zThe processing cost of domestic garbage can be obtained by calculation:3.3 2010-2015 Water Pollution Virtual Governance CostThe next section considers the cost of water pollution control for land use projects. Based on the above two treatment costs, we still choose the virtual treatment cost method to calculate the annual water pollution treatment cost from 2010 to 2015. By looking up relevant materials, we know that water pollution involves a wide range of fields, including planting, livestock and poultry breeding, industry, urban life and rurallife. However, due to the lack of systematic treatment of sewage in agricultural production, we only consider the cost of virtual pollutants in industry and urban life. Here, we consider the water pollution of small community projects as urban life water pollution, while large national projects are considered as industrial sector water pollution.Pollutants in industrial wastewater include COD, ammonia nitrogen, petroleum and heavy metals. Here we consider the virtual governance cost of four pollutants, then the calculation formula of the virtual governance cost of industrial enterprises is:The virtual governance cost of the industrial sector=∑(pollutant emissions× virtual governance costs per unit of pollutants× virtual removal rate of pollutants), the total virtual governance cost is the sum of the virtual governance costs of various pollutants.Virtual domestic wastewater treatment costs include virtual management cost of COD and ammonia nitrogen virtual governance cost, namely virtual wastewater treatment cost = ∑ (pollutant discharge unit pollutant of virtual management cost virtual pollutants removal rate), namely industrial COD discharge quantity A1, industrial ammonia nitrogen emissions B1, C1 oil discharge, heavy metal emissions D1, cities COD discharge E1 and ammonia nitrogen emissions for F1.The cost of treatment of each pollutant is:It should be noted that the virtual removal rate of pollutants is not 100%, the removal rate of COD is 80%, the removal rate of ammonia nitrogen is 50%, the removal rate of petroleum is 80%, and the removal rate of heavy metals is 50%. The annual emissions of various pollutants are:Table 20 the annual emissions of industrial pollutantIndustrial COD annual emissions/10,000 tons Industrialammonia nitrogen annual emissions /10,000 tonsAnnual oil emissions/ 10,000 tons Annual heavy metal emissions / ton2015 293.50 21.70 1.50 313.70 2014 311.30 23.20 1.60 333.70 2013 319.50 24.60 1.70 366.30 Table 19 The cost of treatment of each pollutantUnit COD governance cost 800/ton Unit ammonia nitrogen treatment cost 100/tonUnit oil treatment cost 500/ton Unit heavy metal treatment cost 1000/ton Unit cost statistics (assuming the cost is the same)Then calculated by the above data:Industrial COD Treatment Cost=A1*80%*800Industrial Ammonia Nitrogen Treatment Cost=B1*50%*100Petroleum treatment cost=C1*80%*500Heavy metal treatment cost=D1*90%*1000Urban COD Treatment Cost=E1*80%*800Urban Ammonia Nitrogen Treatment Cost=F1*50%*100 Virtual cost of industrial water pollution=industrial COD treatment cost+ industrial ammonia nitrogen treatment cost+ petroleum treatment cost+ heavy metal treatment cost, namely:Virtual cost of industrial water pollution=industrial COD treatment cost+ industrial ammonia nitrogen treatment cost+ petroleum treatment cost+ heavy metal treatment cost, namely:Table 23 virtual cost of urban water pollution3.4 Total Governance Data AnalysisBased on the above three data, we put the three aspects of governance cost together to form the time governance cost chart, as shown in the figure below:Chart 1 Industrial integrated virtual governance costThe cost of industrial governance in 2011 was relatively larger than that in 2010. However, after 2011, the cost of industrial governance began to decline. Compared with the cost of industrial pollution governance, the cost of living management was much higher. The trend is to increase, then decrease, then increase and then decrease. Overall, the cost of living treatment fluctuates between about 245 billion yuan. According to the overall data trend, we find that both the cost of industrial governance and the cost of living governance are decreasing year by year. The main reason for the decrease is that the negative impacts of land use decrease year by year, which also reflects the global increasing attention to the impact of human activities on ecosystem services.3.5 Model Analysis Over TimeFrom the above data, we use the linear regression method to calculate the trend of the model with time. For the industrial governance cost, we set the year=x, the governance cost(10000yuan)=y, and then use the regression line equation to calculate The coefficients a~, b~:Calculate a~=8000000 b~=-214213The relationship between x and y can be obtained as follows: y=-214213x+8000000, that is, the governance cost decreases with time, so the following trend graph can be obtained:Chart 3 industrial governance costy = -214213x + 80000006,600,000.006,800,000.007,000,000.007,200,000.007,400,000.007,600,000.007,800,000.008,000,000.008,200,000.008,400,000.0020112012201320142015Industrial governance cost chartFor the cost of urban life governance, it can be seen from the above data that the governance cost changes periodically with time. It is preferable to consider the trend of moving average to predict the future cost of governance. Let be the forecast of governance cost for the next year. The number of periods in which n is moving average;is the pre-treatment cost, and represent the actual values of the first three periods of the previous two periods until the first n periods, then:In summary, the trend graph of life governance costs over time can be obtained:Chart 4 Life governance costAs can be seen from the above figure, the cost of urban living governance changes periodically with time, but it is still decreasing.4.ConclusionsBased on our analysis, we draw the following conclusions:In the process of social construction, we must recognize the construction of economic society, the development of land use cannot be separated from the existence of ecosystem services and environmental sustainable development, but should be developed in parallel. Thus in the process of land development and utilization, we should pay attention to protecting the ecological environment and correctly assess the economic cost of land development projects.In the analysis process, we found that the environmental degradation cost of large-scale industrial project construction is declining year by year, and the environmental degradation cost of small-scale project construction is not particularly large each year, we can reasonably extrapolate the results, the decline in environmental degradation costs for large industrial projects may be due to the increased efficiency of industrial waste treatment, as well as the application of clean energy, and more non-high environmentally hazardous industrial projects to replace high environmentally hazardous industrial projects. The changing trend ofenvironmental degradation costs for small-scale project construction also reminds us of the need to find new and more economical ways to deal with domestic pollution.Based on the virtual governance cost analysis, we put forward the policy of open land use in the future: Improving the ecological environment, Promoting sustainable development, Ensuring the normal operation of ecological environment services. In the future land development and construction projects, we should pay more attention to the assessment of ecological services, so that the development and utilization rate of land tends to be highly rationalized.4.1 Strengths●The model simulates the annual virtual governance cost and makes the resultaccurate and reliable.●In selecting the samples, we selected China's annual data on pollution control.China is a representative country. Therefore, the sample is representative and persuasive.●In the measurement model, linear regression and moving average are used tosimplify the model and ensure the rationality of data. As a result, the entire model is relatively easy to implement.●The model is used to simulate the data obtained, and the change trend isrepresented by graph at last, so that the result is more clear.4.2 Weakness●The linear equation is used to simplify the model and make the result moreintuitive, but due to the unpredictability and diversity of some factors, the regression analysis is limited in some cases.●Due to the limited space, we only considered the three main factors for calculatingthe low cost of environmental degradation,which means that there are still some factors that will affect the correctness of the model we built.●We only analyzed the data for six years, which means our results may still beinaccurate.●In addition, our model simulates the situation that unit governance cost remainsunchanged, without taking into account the fact that scientific progress reduces unit governance cost. In fact, this is unscientific.4.3 Model ImprovementIn the construction of the model, we assume that the unit governance cost remains unchanged, but with the progress of science and technology, the pollution governance technology will certainly increase and the governance cost will also decrease, so if this important factor is not evaluated, it is not scientific to make themodel in real life. In addition, our consideration of the model still requires a lot of data to simulate, and we do not use enough data in the validation process. We also need to further add factors that may affect model evaluation to the models we build to make them more practical.5. References[1]Ministry of Environmental Protection of the People's Republic of China. China Environmental Statistics Annual Report [R] China Environmental Press, 2011[2]Ministry of Environmental Protection of the People's Republic of China. China Environmental Statistics Annual Report[R] China Environmental Press, 2012[3]Ministry of Environmental Protection of the People's Republic of China. China Environmental Statistics Annual Report[R] China Environmental Press, 2013[4]Ministry of Environmental Protection of the People's Republic of China. China Environmental Statistics Annual Report[R] China Environmental Press, 2014[5]Ministry of Environmental Protection of the People's Republic of China. China Environmental Statistics Annual Report[R] China Environmental Press, 2015[6]Ministry of Environmental Protection of the People's Republic of China. China Environmental Statistics Annual Report[R] China Environmental Press, 2016[7]Fang Yu,《China Environmental Economic Accounting Technical Guide》，[M],2009[8]Wupeng Du, Qingxian Gao, Enchen zhang, Qilong Miao, Jianguo Wu, The Emission Status and Composition Analysis of Municipal Solid Waste in China,[J],Research of Environmental Sciences,2006,(19)[9]Feng Cai, Gangcai Chen, Feng Peng, Qingling Yang, Shibo Zhao, Sishu Xian, Fei Wu, Quantitative assessment of eco-environmental damage based on virtual diaposal cost approach,[J],Chinese Journal of Environmental Engineering,2015,(9)[10]National Bureau of Statistics: /[11]Ministry of Housing and Urban-Rural Development of the People’s Republic of China (MOHURD):/xytj/tjzljsxytjgb/jstjnj/index.html。
2019美赛B题
问题B：发送无人机：开发空中灾难救援响应系统背景：2017年，袭击美国波多黎各领土的最严重飓风（见附件1）使该岛遭受严重破坏，并造成2900多人死亡。
飓风风暴潮和波浪作用的综合破坏力对建筑物，房屋和道路造成了大面积的破坏，尤其是波多黎各东部和东南沿海地区。
风暴带来的强风和暴雨击倒了波多黎各80％的电线杆和所有输电线路，导致岛上340万居民丧失了电力。
此外，风暴还破坏或摧毁了岛上大部分的蜂窝通信网络。
岛上大部分地区的电力和电池服务的中断持续了数月，而在某些地区时间则更长。
广泛的洪水阻塞破坏了岛上的许多高速公路和道路，使得地面车辆的紧急服务几乎不可能规划和实现他们的路线。
波多黎各的全面破坏程度在一段时间内仍不明确; 数十个地区孤立无援，与外界无法沟通。
对医疗用品，救生设备和治疗紧张的保健诊所，医院急诊室和非政府组织（NG）救济行动的需求。
在一段时间内，由于慢性病患者转向医院和临时避难所接受治疗，对医疗服务的需求继续激增。
问题：非政府组织（NGO）经常面临在自然灾害期间或之后提供充分和及时响应的挑战，例如2017年袭击美国波多黎各领土的飓风。
一个非政府组织 - HELP, Inc - 正试图通过设计一个名为“DroneGo”的可移动灾难响应系统来提高其应急响应能力.Dronone将使用旋翼无人机提供预先包装的医疗用品并提供高分辨率航拍视频侦察。
选定的无人机应该能够同时或单独执行这两项任务 - 医疗供应和视频侦察 - 取决于救援条件和时间安排。
HELP，Inc已经确定了各种候选转子翼无人机，希望您的团队考虑将其用于设计其DroneGo车队（见附件2,3）。
DroneGo的预包装医疗用品，称为医疗包，旨在增加而不是取代受灾害影响的国家内现场医疗援助组织提供的用品。
HELP，Inc计划推出三种不同的医疗包，称为MED1，MED2和MED3。
无人机将在无人机货舱内携带这些医疗包裹，以便运送到选定的地点（见附件4,5）。
2019美国大学生数学建模竞赛赛前模拟成绩
队号成绩评语C_M0148一等奖论文整体模型较好，能解决好问题，有进行改进，图表可视化较好，在正式比赛中为 Finalist 特等奖候选奖B_M0066一等奖能较好地解决问题，需要加强图表的可视化，模型较好地解决问题，在正式比赛中为 Meritorious Winner 一等奖C_M0055一等奖论文整体模型较好，能解决好问题，有进行改进，图表可视化较好，在正式比赛中为 Meritorious Winner 一等奖D_M0138二等奖摘要过长，超过了一页，需要加强可视化，参考文献过少，在正式比赛中为二等奖 Honorable Mentions 奖B_M0135二等奖摘要过长，需要加强可视化，在正式比赛中为二等奖Honorable Mentions 奖B_M0139二等奖需要加强图表的可视化，模型较好地解决问题，在正式比赛中为 Meritorious Winner 一等奖B_M0145二等奖摘要过长，超过了一页，需要加强可视化，参考文献过少，在正式比赛中为二等奖 Honorable Mentions 奖B_M0146二等奖摘要过短，模型简单，需要加强可视化，在正式比赛中为二等奖 Honorable Mentions 奖B_M0157二等奖模型简单，可视化较好，在正式比赛中为二等奖 HonorableMentions 奖C_M0109二等奖模型简单，需要加强可视化，在正式比赛中为二等奖Honorable Mentions 奖B_M0018三等奖公式截图来自文献，均没有正确引用，全文没解决问题，图表也大部分截图来自参考文献，需要注意学术道德规范，在正式比赛中为Unsuccessful Participant 不成功参赛B_M0019三等奖摘要过短，模型过于简单，行间距过大，所做工作较少，在正式比赛中为 Successfully Participation 成功参与奖B_M0076三等奖论文格式不正确，不完整，论文没有完成，在正式比赛中为Successfully Participation 成功参与奖B_M0132三等奖摘要过短，模型过于简单，需要加强可视化，在正式比赛中为二等奖 Honorable Mentions 奖B_M0142三等奖摘要过短，模型过于简单，需要加强可视化，在正式比赛中为 Successfully Participation 成功参与奖B_M0153三等奖模型过于简单，需要加强可视化，在正式比赛中为二等奖Honorable Mentions 奖B_M0154三等奖图片不要截图，模型过于简单，需要加强可视化，在正式比赛中为二等奖 Honorable Mentions 奖B_M0159三等奖摘要过长，超过了一页，需要加强可视化，参考文献过少，在正式比赛中为二等奖 Honorable Mentions 奖C_M0038三等奖论文格式不正确，不符合比赛要求，模型简单，在正式比赛中为 Unsuccessful Participant 不成功参赛2019美国大学生数学建模竞赛赛前模拟成绩C_M0137三等奖摘要过短，模型简单，需要加强可视化，在正式比赛中为二等奖 Honorable Mentions 奖C_M0152三等奖第一问不是画出所有的图，模型过于简单，需要加强可视化，在正式比赛中为 Successfully Participation 成功参与奖C_M0156三等奖模型过于简单，需要加强可视化，在正式比赛中为Successfully Participation 成功参与奖C_M0160三等奖模型简单，需要加强可视化，在正式比赛中为二等奖Honorable Mentions 奖D_M0067三等奖不要截图，太模糊，模型过于简单，需要加强可视化，在正式比赛中为 Successfully Participation 成功参与奖D_M0128三等奖公式不要截图，模型简单，考虑因素较少，需要加强可视化，在正式比赛中为二等奖 Honorable Mentions 奖D_M0134三等奖模型简单，需要加强可视化，在正式比赛中为二等奖Honorable Mentions 奖D_M0150三等奖公式截图来自文献，均没有正确引用，全文没解决问题，图表也大部分截图来自参考文献，需要注意学术道德规范，在正式比赛中为 Unsuccessful Participant 不成功参赛D_M0190三等奖论文排版非常糟糕，公式截图来自文献，均没有正确引用，全文没解决问题，图表也大部分截图来自参考文献，需要注意学术道德规范，在正式比赛中为 Unsuccessful Participant 不成功参赛E_M0155三等奖摘要不符合要求，模型简单，需要加强可视化，在正式比赛中为二等奖 Honorable Mentions 奖F_M0144三等奖摘要过短，模型简单，需要加强可视化，在正式比赛中为二等奖 Honorable Mentions 奖B_M0212不成功参赛论文抄袭，并且不是本赛题的内容，严重违规，在正式比赛中为 Unsuccessful Participant 不成功参赛。
美国数学建模竞赛优秀论文阅读报告
2.优秀论文一具体要求：1月28日上午汇报1）论文主要内容、具体模型和求解算法（针对摘要和全文进行概括）；In the part1, we will design a schedule with fixed trip dates and types and also routes. In the part2, we design a schedule with fixed trip dates and types but unrestrained routes.In the part3, we design a schedule with fixed trip dates but unrestrained types and routes.In part 1, passengers have to travel along the rigid route set by river agency, so the problem should be to come up with the schedule to arrange for the maximum number of trips without occurrence of two different trips occupying the same campsite on the same day.In part 2, passengers have the freedom to choose which campsites to stop at, therefore the mathematical description of their actions inevitably involve randomness and probability, and we actually use a probability model. The next campsite passengers choose at a current given campsite is subject to a certain distribution, and we describe events of two trips occupying the same campsite y probability. Note in probability model it is no longer appropriate to say that two trips do not meet at a campsite with certainty; instead, we regard events as impossible if their probabilities are below an adequately small number. Then we try to find the optimal schedule.In part 3, passengers have the freedom to choose both the type and route of the trip; therefore a probability model is also necessary. We continue to adopt the probability description as in part 2 and then try to find the optimal schedule.In part 1, we find the schedule of trips with fixed dates, types (propulsion and duration) and routes (which campsites the trip stops at), and to achieve this we use a rather novel method. The key idea is to divide campsites into different “orbits”that only allows some certain trip types to travel in, therefore the problem turns into several separate small problem to allocate fewer trip types, and the discussion of orbits allowing one, two, three trip types lead to general result which can deal with any value of Y. Particularly, we let Y=150, a rather realistic number of campsites, to demonstrate a concrete schedule and the carrying capacity of the river is 2340 trips.In part 2, we find the schedule of trips with fixed dates, types but unrestrained routes. To better describe the behavior of tourists, we need to use a stochastic model（随机模型）. We assume a classical probability model and also use the upper limit value of small probability to define an event as not happening. Then we use Greedy algorithm to choose the trips added and recursive algorithm together with Jordan Formula to calculate the probability of two trips simultaneously occupying the same campsites. The carrying capacity of the river by this method is 500 trips. This method can easily find theoptimal schedule with X given trips, no matter these X trips are with fixed routes or not. In part 3, we find the optimal schedule of trips with fixed dates and unrestrained types and routes. This is based on the probability model developed in part 2 and we assign the choice of trip types of the tourists with a uniform distribution to describe their freedom to choose and obtain the results similar to part 2. The carrying capacity of the river by this method is 493 trips. Also this method can easily find the optimal schedule with X given trips, no matter these X trips are with fixed routes or not.2）论文结构概述（列出提纲，分析优缺点，自己安排的结构）；1 Introduction2 Definitions3 Specific formulation of problem4 Assumptions5 Part 1 Best schedule of trips with fixed dates, types and also routes.5.1 Method5.1.1 Motivation and justification5.1.2 Key ideas5.2 Development of the model5.2.1Every campsite set for every single trip type5.2.2 Every campsite set for every multiple trip types5.2.3One campsite set for all trip types6 Part 2 Best schedule of trips with fixed dates and types, but unrestrained routes.6.1 Method6.1.1 Motivation and justification6.1.2 Key ideas6.2 Development of the model6.2.1 Calculation of p(T,x,t)6.2.2 Best schedule using Greedy algorithm6.2.3 Application to situation where X trips are given7 Part 3 Best schedule of trips with fixed dates, but unrestrained types and routes.7.1 Method7.1.1 Motivation and justification7.1.2 Key ideas7.2 Development of the model8 Testing of the model----Sensitivity analysis8.1Stability with varying trip types chosen in 68.2The sensitivity analysis of the assumption 4④8.3 The sensitivity analysis of the assumption 4⑥9 Evaluation of the model9.1 Strengths and weaknesses9.1.1 Strengths9.1.2 Weakness9.2 Further discussion10 Conclusions11 References12 Letter to the river managers3）论文中出现的好词好句（做好记录）；用于问题的转化We regard the carrying capacity of the river as the maximum total number of trips available each year, hence turning the task of the river managers into looking for the best schedule itself.表明我们在文中所做的工作We have examined many policies for different river…..问题的分解We mainly divide the problem into three parts and come up with three different….对我们工作的要求：Given the above considerations, we want to find the optimal。
美赛一等奖论文-中文翻译版
目录问题回顾 (3)问题分析： (4)模型假设： (6)符号定义 (7)4.1---------- (8)4.2 有热水输入的温度变化模型 (17)4.2.1模型假设与定义 (17)4.2.2 模型的建立The establishment of the model (18)4.2.3 模型求解 (19)4.3 有人存在的温度变化模型Temperature model of human presence (21)4.3.1 模型影响因素的讨论Discussion influencing factors of the model (21)4.3.2模型的建立 (25)4.3.3 Solving model (29)5.1 优化目标的确定 (29)5.2 约束条件的确定 (31)5.3模型的求解 (32)5.4 泡泡剂的影响 (35)5.5 灵敏度的分析 (35)8 non-technical explanation of the bathtub (37)Sｕmｍary人们经常在充满热水的浴缸里得到清洁和放松。
本文针对只有一个简单的热水龙头的浴缸，建立一个多目标优化模型，通过调整水龙头流量大小和流入水的温度来使整个泡澡过程浴缸内水温维持基本恒定且不会浪费太多水。
首先分析浴缸中水温度变化的具体情况。
根据能量转移的特点将浴缸中的热量损失分为两类情况:沿浴缸四壁和底面向空气中丧失的热量根据傅里叶导热定律求出；沿水面丧失的热量根据水由液态变为气态的焓变求出。
因涉及的参数过多,将系数进行回归分析的得到一个一元二次函数。
结合两类热量建立了温度关于时间的微分方程。
加入阻滞因子考虑环境温湿度升高对水温的影响,最后得到水温度随时间的变化规律(见图＊*)。
优化模型考虑保持水龙头匀速流入热水的情况。
将过程分为浴缸未加满和浴缸加满而水从排水口溢出的两种情况，根据能量守恒定律优化上述微分方程,建立一个有热源的情况下水的温度随时间变化的分段模型，（见图*＊)接下来考虑人在浴缸中对水温的影响。
数学建模美赛一等奖优秀论文
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Fall in love with your bathtub
Abstract It’s pleasant to go home to take a bath with the evenly maintained temperature of hot water throughout the bathtub. This beautiful idea, however, can not be always realized by the constantly falling water temperature. Therefore, people should continually add hot water to keep the temperature even and as close as possible to the initial temperature without wasting too much water. This paper proposes a partial differential equation of the heat conduction of the bath water temperature, and an object programming model. Based on the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), this paper illustrates the best strategy the person in the bathtub can adopt to satisfy his desires. First, a spatiotemporal partial differential equation model of the heat conduction of the temperature of the bath water is built. According to the priority, an object programming model is established, which takes the deviation of temperature throughout the bathtub, the deviation of temperature with the initial condition, water consumption, and the times of switching faucet as the four ob jectives. To ensure the top priority objective— homogenization of temperature, the discretization method of the Partial Differential Equation model (PDE) and the analytical analysis are conducted. The simulation and analytical results all imply that the top priority strategy is: The proper motions of the person making the temperature well-distributed throughout the bathtub. Therefore, the Partial Differential Equation model (PDE) can be simplified to the ordinary differential equation model. Second, the weights for the remaining three objectives are determined based on the tolerance of temperature and the hobby of the person by applying Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Therefore, the evaluation model of the synthesis score of the strategy is proposed to determine the best one the person in the bathtub can adopt. For example, keeping the temperature as close as the initial condition results in the fewer number of switching faucet while attention to water consumption gives rise to the more number. Third, the paper conducts the analysis of the diverse parameters in the model to determine the best strategy, respectively, by controlling the other parameters constantly, and adjusting the parameters of the volume, shape of the bathtub and the shape, volume, temperature and the motions and other parameters of the person in turns. All results indicate that the differential model and the evaluation model developed in this paper depends upon the parameters therein. When considering the usage of a bubble bath additive, it is equal to be the obstruction between water and air. Our results show that this strategy can reduce the dropping rate of the temperature effectively, and require fewer number of switching. The surface area and heat transfer coefficient can be increased because of the motions of the person in the bathtub. Therefore, the deterministic model can be improved as a stochastic one. With the above evaluation model, this paper present the stochastic optimization model to determine the best strategy. Taking the disparity from the initial temperature as the suboptimum objectives, the result of the model reveals that it is very difficult to keep the temperature constant even wasting plentiful hot
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The UMAP Journal 40 (2–3) (2019) 133–160. c Copyright 2019 by COMAP, Inc. All rights reserved. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice. Abstracting with credit is permitted, but copyrights for components of this work owned by others than COMAP must be honored. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior permission from COMAP.
Summary
Increase in terror attacks has raised demand for safe emergency evacuation plans worldwide. We focus on evacuating the Louvre, the world’s largest art museum. Evacuation is made difficult by the volume and variety of visitors; the Louvre management desires evacuation plans over a broad set of considerations.
Time to Leave the Louvre 135
Time to Leave the Louvre:
A Computational Network Analysis
Vinit Ranjan Junmo Ryang Albert Xue
Duke University Durham, NC USA Advisor: David Kraines
136 The UMAP Journal 40.2–3 (2019)
Restatement of the em
We are tasked with the broad problem of designing an evacuation model for the Louvre that allows exploration of a range of options. Our primary goals are to:
Our model predicts that an evacuation plan using all four public exits could evacuate the Louvre in 24 minutes. Furthermore, while many bottlenecks surround the Pyramid entrance, the entrance itself is not a bottleneck. This property of the Pyramid is crucial in emergencies, since it allows access for emergency personnel. Additionally, securing the Passage Richelieu is critical to evacuation, since its safety is directly linked to the Pyramid’s safety. Keeping these entrances open and useful is imperative to both speed and safety of an evacuation.
We partition the Louvre into sections and build an agent-based model to simulate evacuations in each section. We run simulations over each section to determine a rate by which agents exited. To connect sections, we represent the building as a graph, thereby posing a network flow problem. The strong duality property identifies bottleneck edges in the graph. Simulating blocked passages or new secret exits is simply removal or addition of edges to the graph. Bottleneck identification is our highest priority for public safety.