灰色关联分析在物流中心选址决策中的应用

针对影响物流中心选址因素的特点,提出用灰色关联分析方法对物流中心选址进行决策,并给出了具体的计算方法和实例分析。     

基于GIS分析的风电场宏观选址方法研究与应用

随着全球环境问题日益突出,可再生能源的利用越来越受到人们的关注。其中,风能是一种具有广泛应用前景的清洁能源,而风电场的选址是风能利用的重要环节。该研究以GIS技术为基础,探索风电场宏观选址方法,旨在提高风电场选址的效率和可靠性。该研究首先对风电场宏观选址的影响因素进行总结,其次,利用GIS技术对选址区域地形以及地类因素进行评估,建立风电场宏观选址模型。最后,该研究以河北某地区为例,应用所提出的风电场宏观选址方法进行了实证分析。研究结果表明,该方法能够有效地筛选最适宜建设风电场的区域,具有很好的实用性和可操作性。同时,该方法还有一定的普适性,可以为其他地区的风电场选址提供参考,提升风电场宏观选址总体规划效率。     

基于GIS的城市社区体育“健身圈”设施选址研究

社区体育"健身圈"建设是一项政府工程,其设施的公益性,使用需求的连续分布及无明确设施位置候选点使得"健身圈"设施的选址不同于一般选址问题。基于"健身圈"设施选址的公平性和效率性原则,构建改进的区位集覆盖模型,以求解使设施完全有效覆盖居民区的最少设施点数及其位置。并运用GIS技术,将连续居民区离散化提取需求点,以需求点缓冲区优化设施位置候选点,从而构建设施选址网络,求解选址模型。最后,以无锡市为例,进行应用研究,以文字及二维地图形式形象表达求解结果。对政府落实《全民健身条例》,建设城乡社区体育"健身圈"具有重要意义。     

基于GIS的最大覆盖双层选址问题研究与实现

最大覆盖选址问题在物流配送中具有重要的应用意义.为了充分考虑现有城市道路的网络特性,建立了基于地理信息系统(GIS)的最大覆盖双层选址分配模型.首先,该模型考虑实际路网的起始-目的地(OD)成本与多配送中心协同供给资源的可能性,在选择配送中心的位置使覆盖的总需求量最大化的同时保证较低的运输分配成本;其次,为了确定配送中...     

基于专家评审法的工厂选址模糊决策

工厂选址是在多种约束条件、多种目标要求下的一个复杂决策过程.基于模糊数学中的模糊综合评价法和模糊决策理论,应用专家评审法提出一个通用的厂址选优的多目标模糊排序决策模型.应用所建立的一个模糊评价模型,对英福康(上海)真空仪器有限公司新厂址的选择进行实例分析.结果表明,基于专家评审法的工厂选址模糊决策模型不需要隶属度函数的先验知识,能够成功地解决工程实际问题.     

物流中心选址的一种综合方法

物流中心选址是建立物流系统的关键环节。本文建立了一种针对企业采购与销售的整个上下游物流活动的物流中心选址模型，同时结合模糊评价法以综合考虑选址过程中的非费用因素，对选址方案进行评价，选取最优选址方案。     

基于灰色聚类评估的配送中心选址模糊多目标优化模型

将多属性决策方法与最优化方法相集成,研究了多配送中心选址优化问题。首先采用灰色聚类决策计算各候选地定性属性的综合评估值。再以选定配送中心的评估值均值最大化、系统成本最小化,以及配送中心容量利用率最大化作为3个优化目标,建立一个考虑需求点模糊需求、供应点与候选配送中心容量限制的三级供应链系统多配送中心选址模型。该模型被描述成了一个多目标的非线性混合整数规划模型。采用机会约束规划对模糊需求进行清晰化处理,并应用目标加权的方法将问题转化为单目标问题。通过算例验证了所提模型的可行性。在实际工作中,可根据决策者权重偏好得出令人满意的结果。     

GIS在南京市垃圾填埋场适宜性选址中的应用

本文应用GIS申的空间分析方法，论述了垃圾填埋场选址的一些限制因子和评判标准，构建了基于ArcGIS的加权适宜性模型，确定了南京市垃圾填埋场的适宜区域，为城市规划部门关于垃圾填埋场选址的决策提供了依据。     

基于多层次灰色关联法的污水厂选址决策模型

针对目前城市污水处理厂厂址选择中存在着较大的主观性和不确定性,以及影响因素的多指标、多层次的特点。将灰色关联分析法与层次分析法相结合,运用层次分析法确定评价指标权值,以各方案的综合灰色关联度作为评判准则,建立了厂址选择方案的层次分析灰色关联度耦合模型。将其运用于重庆市奉节公平镇污水处理厂厂址的选择,结果表明该方法克服了传统选址方法的缺点,是一种切实可行的选址决策方法。     

基于GIS的交通噪声综合评价系统研究

针对交通噪声综合评价计算中大量基于空间属性的关系数据处理,提出了利用GIS空间数据管理功能实现交通噪声综合评价的方法.首先对基于GIS的噪声预测模型计算、噪声冲击指数计算,以及数据库设计等关键技术问题进行了研究,然后选择了VB6.0和MapObject作为开发工具,结合Arcview的二次开发搭建了一个系统平台,并通过广州市"天一新村"住宅小区交通噪声综合评价的实践应用,对系统的有效性进行了验证.     

广州可持续发展多目标决策支持算法研究

以广州市为例,采用广州市历史统计数据,对将来的发展进行预测。在此基础上,探讨人口、资源、环境与经济之间的关系,建立了可持续发展多目标决策支持模拟系统。模型的计算结果可供政府及高、中层决策者制定政策时作参考。     

河北省可持续发展决策支持系统研究

阐述了河北省可持续发展评价指标体系及其评价方法，分析了建立河北省可持续发展决策支持系统（HBSDDSS）的意义、系统目标及其设计原则；最后给出了HBSDDSS的总体框架。     

Decision Support System for Maintenance Management Using Bayesian Networks

The maintenance process has undergone several major developments that have led to proactive considerations and the transformation fiom the traditional ＂fail and fix＂ practice into the ＂predict and prevent＂ proactive maintenance methodology. The anticipation action, which characterizes this proactive maintenance strategy is mainly based on monitoring, diagnosis, prognosis and decision-making modules. Oil monitoring is a key component of a successful condition monitoring program. It can be used as a proactive tool to identify the wear modes of rubbing pans and diagnoses the faults in machinery. But diagnosis relying on oil analysis technology must deal with uncertain knowledge and fuzzy input data. Besides other methods, Bayesian Networks have been extensively applied to fault diagnosis with the advantages of uncertainty inference; however, in the area of oil monitoring, it is a new field. This paper presents an integrated Bayesian network based decision support for maintenance of diesel engines.     

项目投标决策支持系统研究

据投标的实际流程，从投标的全过程出发，利用预测理论、决策理论、对策理论和计算机技术等开发了项目投标决策支持系统PBDSS，并系统介绍了PBDSS的结构、功能、实现技术和应用。PBDSS可在施工企业投标工作的信息咨询、项目选择、总价制定、报价内部调整及标书生成等方面发挥辅助决策的作用。     

Intelligent Decision Support System for COVID-19 Empowered with Deep Learning

The prompt spread of Coronavirus (COVID-19) subsequently adorns a big threat to the people around the globe. The evolving and the perpetually diagnosis of coronavirus has become a critical challenge for the healthcare sector. Drastically increase of COVID-19 has rendered the necessity to detect the people who are more likely to get infected. Lately, the testing kits for COVID-19 are not available to deal it with required proficiency, along with-it countries have been widely hit by the COVID-19 disruption. To keep in view the need of hour asks for an automatic diagnosis system for early detection of COVID-19. It would be a feather in the cap if the early diagnosis of COVID-19 could reveal that how it has been affecting the masses immensely. According to the apparent clinical research, it has unleashed that most of the COVID-19 cases are more likely to fall for a lung infection. The abrupt changes do require a solution so the technology is out there to pace up, Chest X-ray and Computer tomography (CT) scan images could significantly identify the preliminaries of COVID-19 like lungs infection. CT scan and X-ray images could flourish the cause of detecting at an early stage and it has proved to be helpful to radiologists and the medical practitioners. The unbearable circumstances compel us to flatten the curve of the sufferers so a need to develop is obvious, a quick and highly responsive automatic system based on Artificial Intelligence (AI) is always there to aid against the masses to be prone to COVID-19. The proposed Intelligent decision support system for COVID-19 empowered with deep learning (ID2S-COVID19-DL) study suggests Deep learning (DL) based Convolutional neural network (CNN) approaches for effective and accurate detection to the maximum extent it could be, detection of coronavirus is assisted by using X-ray and CT-scan images. The primary experimental results here have depicted the maximum accuracy for training and is around 98.11 percent and for validation it comes out to be approximately 95.5 percent while statistical parameters like sensitivity and specificity for training is 98.03 percent and 98.20 percent respectively, and for validation 94.38 percent and 97.06 percent respectively. The suggested Deep Learning-based CNN model unleashed here opts for a comparable performance with medical experts and it is helpful to enhance the working productivity of radiologists. It could take the curve down with the downright contribution of radiologists, rapid detection of COVID-19, and to overcome this current pandemic with the proven efficacy.     

Decision Support System for Evaluating Impact of Product Carbon Labeling Scheme

In this paper a decision support system for systematically evaluating the impact of labeling products with their carbon footprints is developed and applied to prioritize products for carbon labeling in a large supermarket chain in the UK. Carbon labels may change consumers' behavior and encourage suppliers to implement carbon-reduction solutions. Those changes may, however, lead to unintended risks. To handle the challenges of uncertainties in the evaluation, the Evidential Reasoning approach and the Intelligent Decision System software for multi-criteria decision analysis are applied to support the process. The system developed can be applied to assessing the impact of sustainable development policies to maximize their benefits and minimize their risks.     

复杂产品虚拟样机综合集成型决策支持系统研究

现有常规决策方法和决策支持环境难以对复杂问题进行有效的决策支持和求解，复杂产品虚拟样机即属于该类问题，其决策支持环境的设计是虚拟样机工程需要解决的问题之一。在分析当前各种决策支持系统的特点并与综合集成型决策支持系统进行比较后，提出了只有构建综合集成型决策支持系统才能有效地解决复杂产品虚拟样机设计中的决策支持问题。从虚拟样机决策求解的过程分析了系统的需求，建立了B/S架构的、基于移动Agent技术的综合集成型决策支持系统，并阐明了系统的组织结构和功能，对系统实现的关键技术、仿真、信息和知识的综合集成进行了深     

城市重大事故应急辅助决策支持系统研究

针对严重影响我国经济发展和社会稳定的城市重大事故，研究了典型城市重大事故动态模拟的模型和方法，并开发了反应及时、决策科学和资源高效利用的事故应急决策支持系统，主要包括以下功能：城市不同功能区典型重大事故的动态模拟；城市不同功能区典型重大事故应急决策；城市不同功能区、不同事故类型的应急预案库；城市重大事故应急决策与指挥调度决策。该系统充分运用了先进的信息管理手段和工程技术，可及时发现事故隐患、控制事故扩展和蔓延并及时采取救援措施，使财产损失和人员伤亡减少到最低程度。     

A Decision Support System for Determining Optimal Retention Stocks for Service Parts Inventories

This paper reports on the development and successful implementation of a decision support system (DSS) for analyzing service parts inventory retention stocks. The DSS was implemented in a Fortune 100 company with an initial $50,000,000 investment in service parts inventory. During the last two years of use, approximately $13,000,000 of service parts have been scrapped (disposed) with the help of the DSS. Very few of these parts have had to be repurchased from scrap dealers. This has resulted in approximately $6,000,000 in tax savings alone. The system continues 'to be used regularly by the company.

The paper contributes to the service parts management literature in three ways. First, the paper proposes a new forecasting model for the retention stock problem. Second, the paper develops a new inventory model which captures a richer operating environment. Third, the paper suggests how these models may be integrated in an interactive, menu-driven, databased DSS. Although the forecasting model, inventory model, and DSS are described in the context of a specific company, the DSS and the embedded models are applicable to managing service parts in a wide variety of environments.     

工程移民决策支持研究

工程移民指工程建设引起的非自愿的搬迁安置活动，工程移民决策包括规划决策和管理决策，这在国内外都是一个新课题，基于GIS的工程移民DSS是实施科学决策的有效手段，章对工程移民决策支持的理论、方法进行了探讨，并结合万家寨工程移民决策支持进行了试验。