基于($D,U$)控制策略的港口群空箱存储与调运联合优化

针对区域港口群内陆公共腹地的空箱供需状况,构建多港口、多周期空箱存储与调运联合优化混合整数规划模型,利用马尔科夫决策过程与动态规划相结合的方法,在(D,U)库存控制策略下对港口群内各港口的空箱库存保有量区间进行动态优化,以此为基础对港口群公共腹地和各港口间的空箱调运方案进行优化,并以辽宁沿海港口群-----东北腹地为对象进行实证分析.实验结果表明,(D,U)控制策略下的空箱调运能够为船公司节省15.22%的总成本.选取公共腹地数量、公共腹地和港口空箱需求量、单位租箱成本以及单位库存成本进行灵敏度分析,进而验证(D,U)控制策略能够削弱船公司在外界环境发生变化时受到的影响.     

构建卷烟市场价格与社会库存监控体系

建立一个科学的卷烟市场价格与库存监控体系是一项系统工程,要从中国卷烟经营的体制、具体实践相结合.该文从工作实际出发,结合数理统计等数学模型,从作用方式、核心指标、应用模型、运行办法、最佳管理等几个方面述说了构建卷烟市场价格与社会库存监控体系的各个方面.     

数据挖掘技术在钢材仓储管理中的应用研究

提出了数据挖掘技术在钢材仓储管理中的实施方案,并以钢材仓储管理中实时库存的统计分析为例介绍了该方案的实施过程。仓库管理者能够找到有关影响钢材库存损耗的因素,从而改进仓储钢材的利用方法,必然对仓库的效益起到积极的作用。     

数据挖掘技术在钢材仓储管理中的应用研究

提出了数据挖掘技术在钢材仓储管理中的实施方案,并以钢材仓储管理中实时库存的统计分析为例介绍了该方案的实施过程。仓库管理者能够找到有关影响钢材库存损耗的因素,从而改进仓储钢材的利用方法,必然对仓库的效益起到积极的作用。     

含时滞的不确定性供应链网络系统牛鞭效应控制策略及其经济性能分析

考虑需求、生产能力、供应链结构等内外不确定性因素和供应链系统运作延迟,构建了不确定环境下含时滞的供应链库存网络系统状态转移模型.针对牛鞭效应问题,提出了基于库存水平波动状态的控制策略和相应的经济性能指标;借助线性矩阵不等式方法,深入分析库存策略的参数优化设计对牛鞭效应以及经济性能的影响.仿真结果表明,在经济性能约束下,该库存策略具有较强的牛鞭效应遏制能力,从而表明了策略的有效性和实用性.     

港口供应链系统动力仿真模型研究

利用系统动力学,对于港口供应链进行仿真研究。主要构建了港口系统动力学模型,以显示港口供应链的基本构成以及运行情况,根据仿真过程中参数变动分析,揭示瓶颈环节对于供应链运行的较大影响,反映供应链上港口企业的利益相关性。在分析“实际利益”基础上,从供应链的全新视角,探讨港口企业的投资组合策略,这便于港口企业制定明智的投资决策,是对当前投资理念的有利补充。     

基于0-1整数规划的集装箱港口运输环境应用探究

近年来,随着集装箱港口的规模变化、科技的飞速发展,减少成本、提高效率成为集装箱港口的主要发展方向。多机器人系统具有低成本的优势,与路径规划算法相结合,使机器人替代人力进行作业,可以在很大程度上减少港口运营成本。笔者选取2000—2017年的各港口集装箱吞吐量总量、码头泊位数总数以及码头长度总数变化,分析集装箱港口硬件设施发展情况,结合相关影响因素与机器人路径规划算法,提出机器人在运输过程中需要考虑的问题和解决方案,说明机器人可以在港口环境下替代人力进行作业,从而减少港口运营成本,提高工作效率,为集装箱港口面向自动化和智能化提供理论参考。     

海洋高技术产业空间布局优化的动力机制:设计与实施

海洋高技术产业空间布局优化可以提高海洋资源的开发利用度,助推海洋经济的发展,是发展海洋强国的重要部署。研究海洋高技术产业空间布局优化动力是如何影响海洋高技术产业空间布局的,需要引入动力机制来解决。海洋高技术产业空间布局优化的动力源分为内部动力和外部动力,其中,内部动力主要由海洋资源禀赋、基础设施条件和海洋科技发展水平三方面组成,外部动力主要包括金融环境、政府政策、地域品牌等方面。海洋高技术产业空间布局就是在内、外动力的共同作用以及多方面因素的制约下逐步从一种无序的状态变为有序的状态,从而得到优化。最后,通过比较我国与海洋发达国家的差距,针对我国海洋高技术产业缺乏宏观指导、空间分布不合理和产业结构需要优化等问题提出合理化建议。     

基于系统动力学的航材库存管理策略研究

在归纳现有文献对航材周转件库存研究的基础上,分析了影响航材周转件库存的相关因素,构建了因果关系图和系统动力学流图。在此基础上,建立了系统动力学模型,并用建模工具软件Vensim模拟。仿真结果表明：周转件维修水平、库存调节时间是影响库存系统的关键因素,发货延迟对库存系统影响较小。最后,提出了相应的优化航材周转件库存方案。     

配送中心库存控制研究

制造业及销售业将没有竞争力的配送业务外包出去已成为企业的生存之道。配送中心的发展有效减少了交易次数和流通环节、产生规模效益、减少客户库存、提高库存保证程度、控制商品质量等。但配送中心较高的库存也使企业的运营成本居高不下,成为影响其发展的主要顽疾。本文在配送中心库存问题现状研究的基础上,提出配送中心库存控制方面存在的问题,最后,提出控制库存的方法、策略。     

基于主成分分析和指数平滑法的秦皇岛煤炭价格预测

煤炭价格是反映煤炭行业发展状况的重要指标,关系国民经济发展和社会稳定.建立主成分分析和指数平滑模型,分别对影响秦皇岛煤炭价格的主要因素进行排序以及未来一段时间内的煤炭价格进行预测.研究表明:影响秦皇岛煤炭价格的因素顺序依次为:成本要素＞其他能源价格＞进口煤价格＞供需要素＞国家政策＞季节.在未来31天、35周和36个月秦...     

基于多智能集成学习的中短期电煤价格预测

为扩大电力市场交易量与下调市场电价,需要提升电煤价格预测的可靠性与准确性.为此本文提出了多智能集成学习的中短期电煤价格预测方法.首先,阐述了Stacking集成学习的结构和原理;然后,介绍了数种智能电煤价格的预测模型,并通过算例证明了不同单智能模型对数据的感知能力存在差异性;进而,通过比较单智能模型预测结果的差异值均差,筛选出预测性能优异并且数据感知角度差异性明显的智能模型组.为了充分发挥个模型感知能力差异性的优势,利用Stacking融合各模型,得到一种适用于电煤价格滚动预测的集成模型.最后,通过滚动预测2019至2020年的电煤价格,对集成模型的有效性进行验证.     

基于分布式神经网络的苹果价格预测方法

针对传统农产品价格预测模型在大数据场景下无法快速准确对苹果市场价格进行预测的问题,提出一种基于分布式神经网络的苹果价格预测方法。首先,研究影响苹果市场价格的相关因素,选取苹果历史价格、替代品历史价格、居民消费水平和原油价格四个特征作为神经网络模型的输入;然后,构建蕴含价格波动规律的分布式神经网络模型,实现对苹果市场价格的短期预测。实验结果显示,基于分布式神经网络的苹果市场价格短期预测模型具有较高的预测精度,平均相对误差仅为0.50%,满足苹果市场价格预测的要求。实验结果表明,分布式神经网络模型能够通过自学习特性揭示出苹果市场价格的波动规律和发展趋势,所提方法能为稳定苹果市场秩序和市场价格宏观调控提供科学依据,有助于降低价格波动带来的危害,帮助果农规避市场风险。     

A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

中介视角下供需匹配决策方法研究

供需匹配是优化资源利用、形成长期稳定伙伴关系的关键，其本质是寻找或选择合适的供需合作方案。研究针对中介视角下多种货物的供需匹配问题，考虑供应企业关于交易价格的满意度、需求企业关于交易价格和偏好序的满意度、中介视角下供需方各主体的重要程度，以最大化加权供、需方满意度为目标函数，以交易中的具体限制为约束条件，建立基于中介视角的供需匹配多目标决策模型。根据研究问题特点，设计改进的自适应遗传算法对匹配模型求解。以煤矿、煤炭需求企业、港口三方主体间多种煤炭的供需匹配问题为例进行实证研究，计算获得港口视角下供需企业的匹配方案，说明所建立模型和算法的可行性与有效性，能够为客户关系管理研究与应用提供参考与指导。     

A hybrid approach to select the best sourcing policy using stochastic programming

Supply contracts are known as the communication link among supply chain members. As sourcing of required goods is a challenging issue for supply chain members, different sourcing types for different market conditions have been presented in the literature. However, the uncertain price condition has not been much focused in the previous studies, and in the limited works on this issue the correlation between the periods has been ignored. In this paper, sourcing policies are analyzed in a multi-period system in which price and demand follow a Geometric Brownian Motion with drift. Wholesale contract, option contract, and purchase from the spot market are considered as the sourcing alternatives for the buyer. This paper applies the stochastic programming approach to model these three types of sourcing based upon price and demand uncertainties. Afterwards, a hybrid supply model of these sourcing types is developed. By a numerical example, the simulation results of the developed models reveal that each individual sourcing alternative can be selected as the best one in each price and demand behavior. The results also suggest that the proposed hybrid model dominates each of the individual sourcing types. Finally, the paper reports the effects of cost parameter alterations on the solution of the hybrid model through sensitivity analysis.     

基于改进Self-Attention的股价趋势预测

近年来中国经济发展迅速,相应的,中国的金融市场也迅速发展,受到国内外投资者的关注,因此研究中国金融市场上股票价格趋势对学者、投资者和监管者具有重要的意义.随着量化交易等理念的兴起,越来越多的学者将深度神经网络(DNN)应用于金融领域.虽然近几年DNN在图像、语音以及文本等方面已经取得了极大的成功,但其在金融时间序列预测...     

A modeling approach to financial time series based on market microstructure model with jumps

A continuous-time generalized market microstructure (GMMS) model and its discretized model are proposed for characterizing a class of financial time series. The GMMS model is a kind of jump-diffusion model that may describe the dynamic behaviors of measurable market price, immeasurable market excess demand and market liquidity, as well as the interaction among the three variates in a market. The model includes a jump component that is used to capture the large abnormal variations of financial assets, which may occur when a market is affected by some special events happened suddenly, such as release of important financial information. On the basis of the discrete-time GMMS model, an online recursive jump detection algorithm is proposed, which is developed in accordance with the Markov property of financial time series and the Bayes theorem. Simulations and case studies demonstrate the feasibility and effectiveness of the model and its estimation approach presented in this paper.     

Hilbert Spectra and Empirical Mode Decomposition: A Multiscale Event Analysis Method to Detect the Impact of Economic Crises on the European Carbon Market

Exploring the effect of an economic crisis on the carbon market can be propitious to understand the formation mechanisms of carbon pricing, and prompt the healthy development of the carbon market. Through the ensemble empirical mode decomposition (EEMD), a multiscale event analysis approach is proposed for exploring the effect of an economic crisis on the European carbon market. Firstly, we determine the appropriate carbon price data of the estimation and event windows to embody the impact of the interested economic crisis on carbon market. Secondly, we use the EEMD to decompose the carbon price into simple modes. Hilbert spectra are adopted to identify the main mode, which is then used to estimate the strength of an extreme event on the carbon price. Thirdly, we perform a multiscale analysis that the composition of the low-frequency modes and residue is identifying as the main mode to capture the strength of the interested economic crisis on the carbon market, and the high-frequency modes are identifying as the normal market fluctuations with a little short-term effect on the carbon market. Finally, taking the 2007–2009 global financial crisis and 2009–2013 European debt crisis as two cases, the empirical results show that contrasted with the traditional intervention analysis and event analysis with the principle of “one divides into two”, the proposed method can capture the influences of an economic crisis on the carbon market at various timescales in a nonlinear framework.     

基于多元线性回归的螺纹钢价格分析及预测模型

通过分析期货黑色系品种螺纹钢产业链上下游的关系,提出了一种基于多元线性回归分析的螺纹钢价格分析及预测模型。首先,收集 影响螺纹钢价格的主要因素数据,包括焦炭期货结算价、焦煤期货结算价、铁矿石期货结算价、热卷期货结算价与人民币兑美元汇率中间价;然后,通过散点图与趋势线对这些影响因素进行分析以确定影响因素,借助SPSS与NCSS软件利用收集到的数据构建基于最小二乘法的多元线性回归模型,并通过岭回归分析消除自变量间的共线性,得到修正后的模型;最后,运用此模型对未来一个月交易日的螺纹钢价格进行较为精准的预测。实验表明,该模型拟合度较高,具有一定的实用性。