“国培计划”培训团队建设政策的反思与改进--基于政策工具与政策要素的二维分析

培训团队建设是实施“国培计划”的基础工程,是保证“国培计划”实施质量的关键所在。“国培计划”培训团队建设政策是引领“国培计划”培训团队建设实践的重要依据。以2010-2019年“国培计划”培训团队建设政策文本为研究对象,基于政策工具与政策要素的二维分析框架展开研究。研究发现,“国培计划”培训团队建设政策经历了“建章立制”(2010-2012年)、“稳步发展”(2013-2014年)、“精准推进”(2015-2019年)三个阶段,呈现出政策工具差异化、团队成员多元化、运行机制精准化等特征,同时也暴露出政策工具运用失衡、团队建设目标不清晰、相关政策难以有效落地等问题。因此,“国培计划”培训团队建设要注重优化政策工具结构、明确团队建设目标、坚持因地因时制宜,进一步提升政策的长效性、持续性和精准性。     

我国养老机构空间布局特征及其优化

基于ArcGIS软件,运用空间分析法,从公平性和均衡性视角,对全国31个省市（不包括港澳台地区）养老机构的空间布局特征展开研究,探究我国养老机构整体发展历程与演化特点,并利用改进的两步移动搜索法对各县域养老机构的空间可达性进行测算。结果表明：我国养老机构的发展历程可划分为萌芽期（1949—1977年）、增长期（1978—1998年）、规范期（1999—2008年）和建设期（2009—2020年）四个阶段,各阶段具有不同的分布特征;养老机构总体区域分布差异较大,主要集中在东部和中部地区,而西部地区较为分散,供给水平尚未达到预期目标值;我国养老机构空间分布出现多个聚集核心,布局总体呈“品字形”分布,密度由聚集中心向周边地区逐渐递减;按规模划分有效服务半径评价养老机构的空间可达性更为有效,我国东南部地区的可达性明显优于西北部地区,西北部多数为可达性低值区,养老机构资源配置有待进一步完善。     

中国粮食物流体系现代化建设策略

粮食物流体系是连接粮食生产和消费的重要一环,加快推进其现代化建设将有力保障我国的粮食安全。基于对现阶段中国粮食物流体系的全面调研,详细分析了我国粮食物流体系现代化建设的发展现状、存在问题和面临形势,并提出推进粮食物流体系现代化的策略。第一,提升粮食物流体系建设系统化水平,增强粮食流通能力;第二,加快粮食流通立法和标准化建设工作,打破要素流通制约;第三,提高粮食物流设施建设专业化水平,提升物流体系效能;第四,积极开展粮食物流体系信息化改革,优化资源配置效率;第五,统筹各地并优化资金配置,推进项目整体布局;第六,构建粮食流通“双循环”格局,培育国际化大粮商。     

Sampling from spatial databases

This paper deals with techniques for obtaining random point samples from spatial databases. We seek random points from a continuous domain (usually ²) which satisfy a spatial predicate that is represented in the database as a collection of polygons. Several applications of spatial sampling (e.g. environmental monitoring, agronomy, forestry, etc) are described. Sampling problems are characterized in terms of two key parameters: coverage (selectivity), and expected stabbing number (overlap). We discuss two fundamental approaches to sampling with spatial predicates, depending on whether we sample first or evaluate the predicate first. The approaches are described in the context of both quadtrees and R-trees, detailing the sample first, acceptance/rejection tree, and partial area tree algorithms. A sequential algorithm, the one-pass spatial reservoir algorithm is also described. The relative performance of the various sampling algorithms is compared and choice of preferred algorithms is suggested. We conclude with a short discussion of possible extensions.     

Sensitivity of the Optimum to Perturbations of the Profit or Weight of an Item in the Binary Knapsack Problem

In the binary single constraint Knapsack Problem, denoted KP, we are given a knapsack of fixed capacity c and a set of n items. Each item j, j = 1,...,n, has an associated size or weight w_j and a profit p_j. The goal is to determine whether or not item j, j = 1,...,n, should be included in the knapsack. The objective is to maximize the total profit without exceeding the capacity c of the knapsack. In this paper, we study the sensitivity of the optimum of the KP to perturbations of either the profit or the weight of an item. We give approximate and exact interval limits for both cases (profit and weight) and propose several polynomial time algorithms able to reach these interval limits. The performance of the proposed algorithms are evaluated on a large number of problem instances.     

基于工作流的企业业务过程集成建模方法

工作流管理是一种对业务过程进行支持、控制、监视和优化的先进工具. 将生产制造过程 BOM和Petri 网技术应用于企业业务过程,对于相关定义、建模分析以及从BOM到Petri 网的映 射实现算法进行了说明;对基于BOM- Petri 的建模方法进行了扩展优化;最后结合算法进行了 仿真研究.     

Penalty guided genetic search for redundancy optimization in multi-state series-parallel power system

This paper presents a genetic algorithm (GA) for parallel redundancy optimization in series-parallel power systems exhibiting multi-state behavior, optimizing the reliability subject to constraints. The components are binary and chosen from a list of products available in the market, and are being characterized by their feeding capacity, reliability, cost and weight. System reliability is defined as the ability to satisfy consumer demand and is presented as a piecewise cumulative load curve. In GA, to handle infeasible solutions penalty strategies are used. Penalty technique keep a certain amount of infeasible solutions in each generation so as to enforce genetic search towards an optimal solution from sides of, both, feasible and infeasible regions. We here present a dynamic adaptive penalty function which helps the algorithm to search efficiently for optimal/near optimal solution. To evaluate system reliability, a fast procedure, based on universal generating function, is used. An example considering a multi-state series-parallel power system is solved considering both homogeneous and heterogeneous types of redundancy. Also an example considering price discounts is solved. The effectiveness of the penalty function and the proposed algorithm is studied and shown graphically.     

基于两阶段优化算法的神经网络预测模型

采用基于两阶段优化算法(multi-stage optimization approach,MSOA)的GA人工神经网络,将训练集分为两部分,在前一训练集训练后获得的网络基础上使用后一训练集进行进一步的训练获得更为优化的网络结构.针对复杂系统建模输入节点难以确定的问题,提出将其与自组织数据挖掘算法相结合,利用GMDH算法获得神经网络的初始化节点,使用训练好的神经网络模型进行预测.将由此建立的预测模型应用于粮食价格的预测,并进一步探讨了MSOA算法的收敛性问题.结果表明基于GMDH和MSOA的神经网络组合预测模型能较大提高神经网络的全局收敛能力和收敛速度,提高预测精度.     

Inverse Combinatorial Optimization: A Survey on Problems, Methods, and Results

Given a (combinatorial) optimization problem and a feasible solution to it, the corresponding inverse optimization problem is to find a minimal adjustment of the cost function such that the given solution becomes optimum.Several such problems have been studied in the last twelve years. After formalizing the notion of an inverse problem and its variants, we present various methods for solving them. Then we discuss the problems considered in the literature and the results that have been obtained. Finally, we formulate some open problems.     

基于递阶生产系统投入产出模型的企业生产经营决策优化应用的研究

本文基于递阶生产系统投入产出模型,以某锦纶厂为应用背景,在建立销售结构优化模型的基础上,对企业销售结构优化、企业内部潜力挖掘和产品定价等几个方面的经营决策优化问题进行了探讨,并进行了成功的实践。