Multinomial Selection in the Presence of Infinite Alternatives

We propose a new procedure for the multinomial selection problem to solve a real problem of any modern Air Force: the elaboration of better air-to-air tactics for Beyond Visual Range air-to-air combat that maximize its aircraft survival probability H(θ, ω), as well as enemy aircraft downing probability G(θ, ω). In this study, using a low-resolution simulator with generic parameters for the aircraft and missiles, we could increase an average success rate of 16.69% and 16.23% for H(θ, ω) and G(θ, ω), respectively, to an average success rate of 76.85% and 79.30%. We can assure with low probability of being wrong that the selected tactic has greater probability of yielding greater success rates in both H(θ, ω) and G(θ, ω) than any simulated tactic.     

Multi-objective optimization in partitioning the healthcare system of Parana State in Brazil

Motivated by a proposal of the local authority for improving the existing healthcare system in the Parana State in Brazil, this article presents an optimization-based model for developing a better system for patients by aggregating various health services offered in the municipalities of Parana into some microregions. The problem is formulated as a multi-objective partitioning of the nodes of an undirected graph (or network) with the municipalities as the nodes and the roads connecting them as the edges of the graph. Maximizing the population homogeneity in the microregions, maximizing the variety of medical procedures offered in the microregions, and minimizing the inter-microregion distances to be traveled by patients are considered as three objective functions of the problem. An integer-coded multi-objective genetic algorithm is adopted as the optimization tool, which yields a significant improvement to the existing healthcare system map of the Parana State. The results obtained may have a strong impact on the healthcare system management in Parana. The model proposed here could be a useful tool to aid the decision-making in health management, as well as for better organization of any healthcare system, including those of other Brazilian States.     

新时代侨校思想政治工作的价值定位和路径优化

侨校思想政治工作是高校思想政治工作的有机构成。进入新时代，要准确把握侨校思想政治工作的价值定位，直面当前侨校思想政治工作中所面临的价值观念多元化、工作队伍薄弱化和工作对象差异化的挑战，创新思路，统筹着力，牢牢掌握侨校意识形态工作的话语权，汇聚侨校思想政治工作的强大合力，构建分层化差异化的教育模式，增强侨校思想政治工作的时代性、针对性和实效性。     

数据驱动下收货方质量偏好与电商配送服务质量优化

针对"互联网+大数据"优化电商配送服务质量问题，明确不同收货方的质量需求稳定性，引导电商根据收货方不同质量敏感性提供相对精准服务，提升配送服务质量。模拟投票结果的形成过程聚类得到收货方对服务质量偏好的记忆性特征：（1）"无记忆"型收货方；（2）"记忆"型收货方；（3）"不确定"型收货方；（4）收货方总体。进一步推导不同规划类型求解空间，设计得到"无记忆"型收货方动态规划精确求解方法，及其他三种类型近似求解粒子群算法。研究过程中，配送资源质量感知度被嵌入到模型约束；"无记忆"型收货方的质量需求规划问题转化为零库存策略最优解问题，进而证明存在精确解；"记忆"型收货方呈现出对质量感知的累积；"不确定"型收货方模型通过赋值即可得总体收货方表达式。结果表明：数据驱动研究框架借助大数据资源，使得电商更易通过收货方的质量偏好设计更加匹配的配送方案；不确定服务需求得到有效满足，投入成本的利用率更高；通过特征分类的方式，尽可能地抽取能够精确最优化的部分，缩小NP范围，提高整体求解的精确度。     

A new metamodel-based method for solving semi-expensive simulation optimization problems

In this article, a new algorithm for rather expensive simulation problems is presented, which consists of two phases. In the first phase, as a model-based algorithm, the simulation output is used directly in the optimization stage. In the second phase, the simulation model is replaced by a valid metamodel. In addition, a new optimization algorithm is presented. To evaluate the performance of the proposed algorithm, it is applied to the (s,S) inventory problem as well as to five test functions. Numerical results show that the proposed algorithm leads to better solutions with less computational time than the corresponding metamodel-based algorithm.     

On algorithmic construction of maximin distance designs

Maximin distance designs are useful for conducting expensive computer experiments. In this article, we compare some global optimization algorithms for constructing such designs. We also introduce several related space-filling designs, including nested maximin distance designs, sliced maximin distance designs, and general maximin distance designs with better projection properties. These designs possess more flexible structures than their analogs in the literature. Examples of these designs constructed by the algorithms are presented.     

Modified genetic algorithm-based clustering for probability density functions

This article modifies two internal validity measures and applies them to evaluate the quality of clustering for probability density functions (pdfs). Based on these measures, we propose a new modified genetic algorithm called GA-CDF to establish the suitable clusters for pdfs. The proposed algorithm is tested by four numerical examples including two synthetic data sets and two real data sets. These examples illustrate the superiority of proposed algorithm over some existing algorithms in evaluating the internal or external validity measures. It demonstrates the feasibility and applicability of the GA-CDF for practical problems in data mining.     

中国决策情境下的多源流模型及其优化研究

作为当今公共政策研究领域最炙手可热的理论模型之一,约翰·金登的多源流分析理论业已得到国内外学者的广泛应用与认可。在对该模型的相关内容进行简单铺陈后,通过辨析国内外不同的政策主体参与逻辑,进一步厘清了我国公共决策的独有特征。研究重点是在整合提炼西方既有修正思路的基础上,沿袭模型的模糊性假设,结合我国具体实践案例,对多源流模型进行优化与补充。研究表明,中国决策情境下的多源流模型优化主要有提高源流间的融合性、政治源流的核心化、焦点事件的独立化以及特殊情况下的临时决策等四种路径,四种优化与补充路径的有机结合将为进一步提升模型适用性提供帮助。     

上海城市人口总量控制与结构优化研究

上海的人口总量和结构一直与其独特的工业化、城镇化进程密不可分。工业化带动人口发展,人口发展推动城市发展。改革开放以来,上海正处于第四波工业化时期,人口增长较快。在人口结构上,户籍人口老龄化,而外来人口具有就近流动、城城流动、定居化特点,且教育水平逐年提高。从承载力上看,上海人口总量合理,但人口分布的不合理影响了人口结构的发展、城市社会的融合,带来了核心区人口过密、产城融合不畅的问题,以及偏好"人脑"、排斥"人手"的问题。面对人口老龄化和可能的人口逆流,上海应稳定人口总量,建设整体推进的多中心发展人口分布格局,完善户籍制度改革,破解"新二元结构",推动社会保障和公共服务均等化,同时结合政府改革,完善相关配套改革措施。     

LC滤波器设计的优化解析公式

给出了改进的Chebyshev和Butterworth低通原型LC滤波器的解析设计公式.此公式充分利用阶数取整带来的余量,在不改变滤波器阶数的前提下减小了通带波动,极大地方便了集成滤波器的实现.给出了集成有源高通滤波器设计实例,验证了改进的Chebyshev和Butterworth低通原型LC滤波器解析设计公式的有效性.