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。     

新时代法治信仰建设:价值指向、心理机制与实践路径

法治信仰是现代法律信仰的核心,是对社会主义法治理念的充分信任和法律制度的自觉遵从。习近平法治思想作为我国法治建设的指导思想,为新时代中国特色社会主义法治信仰建设提供了价值指向,要以“五个坚持”为指导,营造有利于法治信仰生成的政治环境。从法治信仰的心理机制来看,人民群众在对法治价值有了初步认可后,便在价值体验的催化下形成法治价值观,继而在科学公正的立法、执法、司法、守法过程中坚定法治信念,实现法治信仰的升华。要引导全体人民拥护法治、做社会主义法治的忠实崇尚者和捍卫者,在立法、执法、司法以及法治宣传等实践中大力推进新时代法治信仰建设。     

国家治理中的第三次分配:美国镜鉴与中国道路

第三次分配在中国推动共同富裕迈入新阶段的当下扮演着重要角色。基于国家社会关系视角,将第三次分配划分为强政府大社会、弱政府大社会、强政府小社会、弱政府小社会4种实践模式。美国第三次分配建构起了弱政府大社会实践模式,以社会主导、政府监管、政治中立为主要特征。工具理性是美国第三次分配的成功经验,形成了文化、组织、制度及技术“四位一体”的逻辑理路。隐藏在工具理性外衣下的阶级理性是美国第三次分配的失败教训,在阶级理性思维影响下,社会“私”的方面被放大,形成了以捐赠者为中心的家长制作风。中国第三次分配是先富带动后富、助推共同富裕的实现方式,它深嵌到中国式现代化进程中。中国第三次分配不能照搬美国模式,需要在充分吸收工具理性和摒弃阶级理性的经验教训基础上,塑造强政府大社会模式,将中国文化要素融入制度体系,增强第三次分配主体规范性,加大第三次分配激励力度,完善第三次分配监管体系,持续并高度关注贫困人群。     

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。