基于物联网技术的智能交通控制系统研究

作为物物相连的互联网,物联网通过信息传感设备,把互联网与任何物品相连接,为构建智能交通信号控制与采集的体系提供了可能。论文从基于物联网的智能交通系统整体框架入手,并着重分析其在交通控制和信号采集两个子系统中的运用,指出物联网技术将全面提升交通管理水平。     

人工智能技术辅助开发“智铸”系统

采用开源免费的深度学习平台,利用python二次开发了“智铸”系统,包括“识铸”“听铸”“盯铸”三个模块,铸造企业员工可以用手机或电脑自主学习,提升员工从业能力。在设备旁安装“听铸”软硬件一体化设备,可以实现对连续运行设备的在线自动监听侦测。“盯铸”模块实现连续生产铸件的自动检测、铸造缺陷分析与重点安全区域的监控。     

中国邮储银行大连分行网点转型下客户服务模式创新研究

随着社会大众对金融服务的要求不断增多,银行网点转型已经成为一个必然趋势。信息时代背景下,银行网点的转型大多向智能化的方向转变,而随着网点转型,其客户服务模式也在不断创新。论文以邮储银行大连分行为例,对银行网点智能化转型的影响进行了分析,探究了邮储银行大连分行网点智能化转型存在的问题,并且提出了相关的转型建议,最后对邮储银行大连分行智能化网点转型下客户服务模式的创新情况进行了研究,希望能够在银行网点智能化转型的背景下,促进其客户服务水平的不断提升。     

Optimizing a portfolio of mean-reverting assets with transaction costs via a feedforward neural network

Optimizing a portfolio of mean-reverting assets under transaction costs and a finite horizon is severely constrained by the curse of high dimensionality. To overcome the exponential barrier, we develop an efficient, scalable algorithm by employing a feedforward neural network. A novel concept is to apply HJB equations as an advanced start for the neural network. Empirical tests with several practical examples, including a portfolio of 48 correlated pair trades over 50 time steps, show the advantages of the approach in a high-dimensional setting. We conjecture that other financial optimization problems are amenable to similar approaches.     

Research on a stock-matching trading strategy based on bi-objective optimization

In recent years, with strict domestic financial supervision and other policy-oriented factors, some products are becoming increasingly restricted, including nonstandard products, bank-guaranteed wealth management products, and other products that can provide investors with a more stable income. Pairs trading, a type of stable strategy that has proved efficient in many financial markets worldwide, has become the focus of investors. Based on the traditional Gatev–Goetzmann–Rouwenhorst (GGR, Gatev et al., 2006) strategy, this paper proposes a stock-matching strategy based on bi-objective quadratic programming with quadratic constraints (BQQ) model. Under the condition of ensuring a long-term equilibrium between pairedstock prices, the volatility of stock spreads is increased as much as possible, improving the profitability of the strategy. To verify the effectiveness of the strategy, we use the natural logs of the daily stock market indices in Shanghai. The GGR model and the BQQ model proposed in this paper are back-tested and compared. The results show that the BQQ model can achieve a higher rate of returns.     

A Novel Compact Cat Swarm Optimization Based on Differential Method

ABSTRACT

A compact cat swarm optimization scheme (cCSO) is proposed in this paper, which is designed to solve application domains plagued with limited memory and less-computation power, as a member of cat swarm optimization algorithms (CSO), it composes of two sub-modes, i.e., tracing and seeking modes, so it keeps the same search logic of CSO. On the other hand, cCSO inherits the main feature of compact algorithms, a normal probabilistic model is used to represent the population of solutions instead of processing an actual population, which ensures the cCSO to have the modest memory requirement. The updating vector for the probabilistic model provides a clear moving direction for cats in next step. A cat without historical position and velocity is applied in the algorithm. When the cat is in seeking mode, it employs a differential operator to update the cat’s position, which makes it possible for the cat to have multiple searching directions. Experimental results show that cCSO has pretty performance compared with respect to some population-based testing benchmarks. And it also shows superior performance in convergence rate to some compact optimization algorithms. The case study of gray image segmentation proves that it suits for solving the optimization problem by limited hardware.     

Robust scheduling for large scale evacuation planning

It is crucial to develop appropriate strategies to reduce the evacuation time in a disaster situation. The negative impact of large scale disasters can be mitigated by proactive and efficient (time optimal) evacuation planning. The present study aims to develop strategies for public transit-based evacuation for better control and reduced congestion. Mathematical models are formulated for both strategic and operational aspects of evacuation planning to result in efficient, optimal evacuation. The study also presents methods to manage the external environment uncertainties, in particular, evacuation demand uncertainty, by providing robust solutions. To test the efficacy of the models, a case study for a radiological accident in a nuclear plant in India is presented. The results of the case study demonstrate that the models can provide live, efficient and robust results during actual emergencies in acceptable time.     

供应链视角下餐饮O2O模式的配送路径优化

O2O最后一公里配送往往需要综合考虑时间和成本的关系,研究平台对订单的分配和路径优化对提升O2O供应链的整体效率和产业链升级具有十分重要的意义。从餐饮商家和O2O平台两个主要参与者出发对物流配送系统进行研究,提出了适合于O2O餐饮供应链的配送网络,并考虑道路阻塞、最大里程数等约束,搭建了上层模型最短时间、下层模型最低成本的双层规划模型,进而通过NSGA-Ⅱ算法求得可行解,最后验证了算法的可行性,为进一步研究O2O配送提供了新的解决思路。     

石油海运领域运筹优化方法应用研究综述

为研究运筹方法在石油海运领域的应用及前景,检索1980年至今的国内外相关文献,系统归纳提出石油海上运输优化问题的场景分类:海上运输优化、在港作业调度优化以及油船驳运,分析并对比了各研究的模型差异及算法优劣,最后提出了全局优化调度、根据实际添加特殊约束和设置变量、利用规划约束及人工智能等方式弥补线性规划求解的不足等研究建议。     

航班计划优化研究概述

从航班计划优化的不同时间阶段分析,可以将航班计划优化分为航班计划静态编排优化、基于航班延误预测的航班计划动态反馈优化和基于机场协同决策(A-CDM)的航班计划动态调整角度三类;进而从航班时刻、机型指派、航班频率等编制环节分析了航班计划静态编排优化;随后利用延误波及预测与数据挖掘预测的优化方法分析了基于航班延误预测的航班计划动态反馈优化的相关研究。最后,根据航班计划优化复杂性分析,给出了航班计划优化的发展趋势和未来研究方向。