Energy-efficient receding horizon trajectory planning of high-speed trains using real-time traffic information

Optimal trajectory planning of high-speed trains (HSTs) aims to obtain such speed curves that guarantee safety, punctuality, comfort and energy-saving of the train. In this paper, a new shrinking horizon model predictive control (MPC) algorithm is proposed to plan the optimal trajectories of HSTs using real-time traffic information. The nonlinear longitudinal dynamics of HSTs are used to predict the future behaviors of the train and describe variable slopes and variable speed limitations based on real-time traffic information. Then optimal trajectory planning of HSTs is formulated as the shrinking horizon optimal control problem with the consideration of safety, punctuality, comfort and energy consumption. According to the real-time position and running time of the train, the shrinking horizon is updated to ensure the recursive feasibility of the optimization problem. The optimal speed curve of the train is computed by online solving the optimization problem with the Radau Pseudo-spectral method (RPM). Simulation results demonstrate that the proposed method can satisfy the requirements of energy efficiency and punctuality of the train.     

Intelligent driving methods based on expert knowledge and online optimization for high-speed trains

In the engineering control practice of High-Speed Train (HST), the traditional automatic driving method increases the energy consumption and impairs the intelligence of train operation. Different from previous studies, we propose the intelligent driving methods (IDMs), including expert knowledge system and online optimization algorithms, to achieve the multi-objective (safety, punctuality, energy efficient, passengers’ riding comfort, and so on) control of HST. First, we establish the expert knowledge system based on the driving data and control rules of excellent drivers. Then, in order to enhance the adaptability and real-time performance of proposed IDMs, two online optimization algorithms, including exact online programming driving (EOPD) and inexact online programming driving (IOPD), are developed by improved gradient descent and stochastic meta-decent method to update the controller’s output online. Finally, using the field data collected from Beijing-Shanghai High-Speed Railway, the proposed IDMs are verified under the real speed-limit conditions. The simulation results show that EOPD and IOPD can achieve better performances than automatic driving method based on ATO, Fuzzy PID controller and traditional multi-objective optimization method, especially in passengers’ riding comfort and energy-consumption. Furthermore, as the step size is selected with wide randomness in the updating process, IOPD has more operating mode switching times than EOPD but its punctuality is better.     

Specifying safety-critical systems with a decidable duration logic

Punctual timing constraints are important in formal modelling of safety-critical real-time systems. But they are very expensive to express in dense time. In most cases, punctuality and dense-time lead to undecidability. Efforts have been successful to obtain decidability; but the results are either non-primitive recursive or nonelementary. In this paper we propose a duration logic which can express quantitative temporal constraints and punctuality timing constraints over continuous intervals and has a reasonable complexity. Our logic allows most specifications that are interesting in practice, and retains punctuality. It can capture the semantics of both events and states, and incorporates the notions duration and accumulation. We call this logic ESDL (the acronym stands for Event- and State-based Duration Logic). We show that the satisfiability problem is decidable, and the complexity of the satisfiability problem is NEXPTIME. ESDL is one of a few decidable interval temporal logics with metric operators. Through some case studies, we also show that ESDL can specify many safety-critical real-time system properties which were previously specified by undecidable interval logics or their decidable reductions based on some abstractions.     

An operation synchronization model for distribution center in E-commerce logistics service

This paper is among the first that proposes a synchronization measurement model for the distribution centre operation synchronization (DCOS) problem, which aims to ensure the E-commerce order’s punctuality and synchronization at the same time. The main motivation of DCOS is that the intensified competition in E-commerce market makes efficient E-commerce logistics service extremely important, which means saving logistics cost and ensuring customer service at the same time. The synchronized operation may be a possible solution to ensure efficient order transhipment in the distribution center and to save cost. We thus introduce a measurement approach that is able to address the distribution center operation synchronization (DCOS) problem such as the trade-off relationship between synchronization and punctuality. In order to get persuasive conclusions, we adopt data from a real practice case and apply CPLEX to get the optimal solution. Our computational results show that considering the asynchronous cost in the total cost objective function will greatly improve the operation synchronization in the distribution center, by saving the storage space, the equipment, and the labour resources. And if the storage cost is in a reasonable range, the synchronized operation can be realized while the punctuality is also optimized. It is found in our case that the most efficient way to improve distribution center operation is expanding inbound operation capacity.     

Performance assessment of heat exchanger using intelligent decision making tools

Process and manufacturing industries today are under pressure to deliver high quality outputs at lowest cost. The need for industry is therefore to implement cost savings measures immediately, in order to remain competitive. Organizations are making strenuous efforts to conserve energy and explore alternatives. This paper explores the development of an intelligent system to identify the degradation of heat exchanger system and to improve the energy performance through online monitoring system. The various stages adopted to achieve energy performance assessment are through experimentation, design of experiments and online monitoring system. Experiments are conducted as per full factorial design of experiments and the results are used to develop artificial neural network models. The predictive models are used to predict the overall heat transfer coefficient of clean/design heat exchanger. Fouled/real system value is computed with online measured data. Overall heat transfer coefficient of clean/design system is compared with the fouled/real system and reported. It is found that neural net work model trained with particle swarm optimization technique performs better comparable to other developed neural network models. The developed model is used to assess the performance of heat exchanger with the real/fouled system. The performance degradation is expressed using fouling factor, which is derived from the overall heat transfer coefficient of design system and real system. It supports the system to improve the performance by asset utilization, energy efficient and cost reduction in terms of production loss. This proposed online energy performance system is implemented into the real system and the adoptability is validated.     

R-dPSO算法及其在ATO控制策略中的应用

城市轨道交通列车自动运行中,通过调整列车的5种工况序列解决含有安全、准点、准时、舒适度和能耗等指标的多目标优化问题。根据轨道交通列车自动运行过程中涉及的动力学公式建立ATO目标速度曲线的数学模型。提出一种随机驱动的全局粒子群优化算法（R-dPSO）,用12个基准函数测试了R-dPSO算法的有效性。进而,利用SPSO算法、XEPSO算法和R-dPSO算法解决上述多目标优化问题。实验表明,只有R-dPSO算法的优化结果满足ATO控制策略的各个指标要求。     

基于数据仓库的货物进出口决策支持系统

在研究数据仓库的特点、组成及其实现的基础上,对数据仓库和联机分析处理技术进行了研究,构建了一种基于数据仓库的货物进出口决策支持系统结构.解决了数据仓库模型的创建、数据的增量更新及数据分析与展现问题.最后,对基于数据仓库的货物进出口决策支持系统的实现问题进行了探讨.     

面向问题分析与决策的专家系统

专家系统的根本目标在于为实际应用问题提供强有力的分析与决策能力。以人类通过长期实践活动总结的复杂问题分析与决策方法为指导思想,建立了以问题对象为核心、相关对象为问题主体、问题现象为表现形式、因果关系为问题变化驱动力、过程知识和原理知识为参考对象的面向问题分析与决策的专家系统。这种专家系统围绕应用领域中的问题构建知识库,而不是使用规则,所以得到的知识系统比较合理、清晰,不容易产生知识矛盾与冲突,有利于大型知识库的构建;同时,采用基于问题的推理,与人类的思维习惯相符合,可以大大提高推理效率;此外,开发这种专家     

基于改进的代价敏感决策树的网络贷款分类

在网络贷款用户数据集中，贷款成功和贷款失败的用户数量存在着严重的不平衡，传统的机器学习算法在解决该类问题时注重整体分类正确率，导致贷款成功用户的预测精度较低。针对此问题，在代价敏感决策树敏感函数的计算中加入类分布，以减弱正负样本数量对误分类代价的影响，构建改进的代价敏感决策树；以该决策树作为基分类器并以分类准确度作为衡量标准选择表现较好的基分类器，将它们与最后阶段生成的分类器集成得到最终的分类器。实验结果表明，与已有的常用于解决此类问题的算法（如MetaCost算法、代价敏感决策树、AdaCost算法等）相比，改进的代价敏感决策树对网络贷款用户分类可以降低总体的误分类错误率，具有更强的泛化能力。     

A decision support system for optimizing dynamic courier routing operations

In this paper, we propose a prototype of a decision support system (DSS) that integrates a hybrid neighborhood search algorithm to solve the offline and online routing problems arising in courier service. In the dynamic operational environment of courier service, new customer orders and order cancellations continually arrive over time and thus disrupt the optimal routing schedule that was originally designed. This calls for the real-time re-optimization of routes. As service level is sensitive to whether allowable service time intervals are wide or narrow, it is valuable to study how adjustable and flexible time windows influence the courier service efficiency in a dynamic environment. To capture these dynamic features, a dynamic vehicle routing problem (DVRP) that simultaneously considers new customer orders and order cancellations is investigated in this study. Meanwhile, fuzzy time windows are formulated in the DVRP model to quantify the service level and explore the service efficiency. To tackle the new problem, we propose a competitive hybrid neighborhood search heuristic for (re)optimizing the offline and online routes. Numerical computational experiments and the comparison with results from Lingo show that our algorithm is capable of re-optimizing dynamic problems effectively and accurately in a very short time. The proposed model and algorithms are able to enhance courier service level without further expense of a longer traveling distance or a larger number of couriers.     

Two criteria in multiple decision making

The author obtains two solutions for the uncertainty problem in a multistep decision-making problem for a wide class of preference choice rules in a decision-making system. They are based on the principles of guaranteed and best results, respectively, with the criteria in the form of preferences on decisions defined by an explicitly specified utility function, which parametrically depends on a convex statistical regularity on the set of states and on the utility function on the consequences, which is determined to within a positive linear transformation.     

基于数据仓库技术的CRM决策支持系统模型设计

针对目前企业决策支持系统面临的新问题，介绍了CRM相关知识和决策支持系统的前沿技术——数据仓库及数据挖掘和联机分析处理，并在此基础上，提出了以数据仓库为中心、数据挖掘和联机分析处理为手段的面向客户关系管理的决策支持系统模型框架，描述了CRM数据运作流程和数据仓库等技术在其中所起的重要作用。     

类纸阅读器在线读物系统的研究与实现

针对类纸阅读器中在线读物的排版和网络延时问题,提出一种面向嵌入式系统的在线读物系统。利用启发式规则对在线读物内容进行过滤和二次排版,使当前阅读内容的排版适应类纸屏幕,并对其进行缓存与预取,为用户提供本地化的阅读体验。实验结果表明,该系统可提高类纸阅读器中在线读物的阅读有效性。     

An Advanced Real-Time Train Dispatching System for Minimizing the Propagation of Delays in a Dispatching Area Under Severe Disturbances

In highly utilized rail networks, as in the Netherlands, conflicts and subsequent train delays propagate considerably in time and space during operations. In order to realistically forecast and minimize delay propagation, there is a need to extend short-term traffic planning up to several hours. On the other hand, as the magnitude of the time horizon increases the problem becomes computationally intractable and hard to tackle. In this paper, we decompose a long time horizon into tractable intervals to be solved in cascade with the objective of improving punctuality. We use the ROMA dispatching system to pro-actively detect and globally solve conflicts on each time interval. The future evolution of railway traffic is predicted on the basis of the actual track occupation, the Dutch signaling system and dynamic train characteristics. Extensive computational tests are carried out on the railway dispatching area between Utrecht and Den Bosch.     

混合分布估计算法求解考虑同步性和准时性的三阶段装配集成调度问题

本文研究以加工–运输–装配同步性和交货准时性的加权和为优化目标的三阶段装配集成调度问题(3sAISP_SP),并基于问题特点设计混合分布估计算法(HEDA)进行求解.首先,分别建立3sAISP SP的数学规划模型和排列模型.其次,在对问题模型特点分析的基础上,设计合理的编码和解码规则,同时利用HEDA中基于概率模型的全局搜索以发现问题解空间存在优质解的区域.然后,为进一步提高算法性能,设计3种局部搜索策略对优质解区域进行细致搜索.进而,在小规模问题下,将HEDA得到的较优解与优化求解器GUROBI得到的最优解进行比较,验证HEDA的求解结果接近最优解;在较大规模问题下,将HEDA与其他有效智能优化算法进行比较,验证HEDA的求解性能.最后,通过对优化目标中不同权重设置的实验分析,给出加工–运输–装配同步性和交货准时性权重设置的合理范围,并得到考虑装配同步性有利于降低中间库存的结论.     

Logistics service provider selection decision making for healthcare industry based on a novel weighted density-based hierarchical clustering

In recent years, Industry 4.0 makes a significant impact on the manufacturing industry, which enables the business more intelligent and efficient, all while minimizing costs. As known, the logistics concerns in the supply chain always play an important role to a manufacturing company, and decision on the selection of logistics service provider is a key point, especially for healthcare manufacture whose products are the medical devices or equipment of fragility and high cost. Practically there are so many logistics service providers with varieties in service quality, effectiveness, punctuality and reliability, that the manufacturers often encounter the challenge on the provider selection, and healthcare industry is no exception. However, the research on provider selection for healthcare manufacturers is quite limited. In order to help them to make the decision, this paper designs a logistics service provider selection scheme based on a novel weighted density-based hierarchical cluster analysis with integration of the analytic hierarchy process (AHP) for healthcare industry. Initially an evaluation index system reflecting the capability of the candidate providers in all aspects is established. To improve the clustering within the scheme, the density concept and the obtained weights are introduced into the traditional hierarchical cluster analysis (HCA) to shape a novel Weighted Density-Based HCA (WDBHCA). To validate the feasibility of the scheme, a case study on a specified healthcare industry manufacturer is carried out, and results fulfill the case company’s requirement which shows the feasibility of the proposed provider selection scheme. In addition, this scheme can be applied to the provider selection in other fields, as well.     

Hellinger distance decision trees are robust and skew-insensitive

Learning from imbalanced data is an important and common problem. Decision trees, supplemented with sampling techniques, have proven to be an effective way to address the imbalanced data problem. Despite their effectiveness, however, sampling methods add complexity and the need for parameter selection. To bypass these difficulties we propose a new decision tree technique called Hellinger Distance Decision Trees (HDDT) which uses Hellinger distance as the splitting criterion. We analytically and empirically demonstrate the strong skew insensitivity of Hellinger distance and its advantages over popular alternatives such as entropy (gain ratio). We apply a comprehensive empirical evaluation framework testing against commonly used sampling and ensemble methods, considering performance across 58 varied datasets. We demonstrate the superiority (using robust tests of statistical significance) of HDDT on imbalanced data, as well as its competitive performance on balanced datasets. We thereby arrive at the particularly practical conclusion that for imbalanced data it is sufficient to use Hellinger trees with bagging (BG) without any sampling methods. We provide all the datasets and software for this paper online ().     

Automating the synthesis of decision procedures in a constructive metatheory

We present an approach to the automatic construction of decision procedures, via a detailed example in propositional logic. The approach adapts the methods of proof‐planning and the heuristics for induction to a new domain, that of metatheoretic procedures. This approach starts by providing an alternative characterisation of validity; the proofs of the correctness and completeness of this characterisation, and the existence of a decision procedure, are then amenable to automation in the way we describe. In this paper we identify a set of principled extensions to the heuristics for induction needed to tackle the proof obligations arising in the new problem domain and discuss their integration within the clam‐Oyster system. This revised version was published online in June 2006 with corrections to the Cover Date.     

机场加油车指挥调度系统的设计与实现

为解决机场加油服务环节,提高航班正点率,讨论了一个机场加油车指挥调度系统的设计与实现。该系统综合利用了先进的信息技术、网络技术、无线数字通讯技术、嵌入式控制系统、GPS等,来提高机场加油服务的效率和安全性。描述了机场加油车指挥调度系统的业务需求,介绍了该系统的体系结构设计和各子系统的主要功能,给出了系统流程及实现情况。实际应用表明,该系统提高了服务效率,降低了营运成本,有一定的推广前景。     

Online adaptive decision trees

Decision trees and neural networks are widely used tools for pattern classification. Decision trees provide highly localized representation, whereas neural networks provide a distributed but compact representation of the decision space. Decision trees cannot be induced in the online mode, and they are not adaptive to changing environment, whereas neural networks are inherently capable of online learning and adpativity. Here we provide a classification scheme called online adaptive decision trees (OADT), which is a tree-structured network like the decision trees and capable of online learning like neural networks. A new objective measure is derived for supervised learning with OADT. Experimental results validate the effectiveness of the proposed classification scheme. Also, with certain real-life data sets, we find that OADT performs better than two widely used models: the hierarchical mixture of experts and multilayer perceptron.