Vehicle routing optimization algorithm based on time windows and dynamic demand

To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,based on the ideas of pre-optimization and real-time optimization,a two-stage planning model of dynamic demand based vehicle routing problem with time windows was established.At the pre-optimization stage,an improved genetic algorithm was used to obtain the pre-optimized distribution route,a large-scale neighborhood search method was integrated into the mutation operation to improve the local optimization performance of the genetic algorithm,and a variety of operators were introduced to expand the search space of neighborhood solutions;At the real-time optimization stage,a periodic optimization strategy was adopted to transform a complex dynamic problem into several static problems,and four neighborhood search operators were used to quickly adjust the route.Two different scale examples were designed for experiments.It is proved that the algorithm can plan the better route,and adjust the distribution route in time under the real-time constraints.Therefore,the proposed algorithm can provide theoretical guidance for suppliers to solve the dynamic demand based vehicle routing problem.

Improved ant colony optimization for multi-depot heterogeneous vehicle routing problem with soft time windows

Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.

基于Real-Time Windows Target状态观测器的实验开发

在Matlab/Simulink中应用Real-Time Windows Target模块重构状态观测器的实时仿真模型,采用该方法开发的实验系统既便于实现,又能和实际物理系统良好对接,有利于学生掌握状态观测器的相关知识和促进对Matlab的了解和应用。给出的应用实例验证了方法的可行性。

Whole-brain CT Perfusion at Admission and During Delayed Time-window Detects the Delayed Cerebral Ischemia in Patients with Aneurysmal Subarachnoid Hemorrhage

Objective To evaluate the utility of computed tomography perfusion(CTP)both at admission and during delayed cerebral ischemia time-window(DCITW)in the detection of delayed cerebral ischemia(DCI)and the change in CTP parameters from admission to DCITW following aneurysmal subarachnoid hemorrhage.Methods Eighty patients underwent CTP at admission and during DCITW.The mean and extreme values of all CTP parameters at admission and during DCITW were compared between the DCI group and non-DCI group,and comparisons were also made between admission and DCITW within each group.The qualitative color-coded perfusion maps were recorded.Finally,the relationship between CTP parameters and DCI was assessed by receiver operating characteristic(ROC)analyses.Results With the exception of cerebral blood volume(P=0.295,admission;P=0.682,DCITW),there were significant differences in the mean quantitative CTP parameters between DCI and non-DCI patients both at admission and during DCITW.In the DCI group,the extreme parameters were significantly different between admission and DCITW.The DCI group also showed a deteriorative trend in the qualitative color-coded perfusion maps.For the detection of DCI,mean transit time to the center of the impulse response function(Tmax)at admission and mean time to start(TTS)during DCITW had the largest area under curve(AUC),0.698 and 0.789,respectively.Conclusion Whole-brain CTP can predict the occurrence of DCI at admission and diagnose DCI during DCITW.The extreme quantitative parameters and qualitative color-coded perfusion maps can better reflect the perfusion changes of patients with DCI from admission to DCITW.

基于Windows/RTX的实时仿测软件设计

针对传统测试软件实时性有限、传统仿真接口软件通用性程度低的问题,为满足半实物仿真软件需兼顾单元测试和控制系统仿真验证的需求,设计了基于Windows/RTX的实时仿测软件。仿测软件采用模块化的设计原则,开发了GUI层人机交互界面和RTX层实时运行程序。为保证实时性,采用无锁循环缓冲区+双线程技术,解决了RTX环境下仿真步长为1 ms时串口数据收发的超时问题;提出一种超时检测算法监测仿真节点的实时状态。借助cJSON优化了测试用例配置文件,用户可以更灵活地编辑测试用例,利用RTW自动代码生成将弹体模型编译集成到RTX仿测软件工程。实验结果表明:该仿测软件有效兼顾了单测与仿真,提高了仿测软件的通用性和二次开发效率,降低了开发难度。

Multi-type ant system algorithm for the time dependent vehicle routing problem with time windows

The time dependent vehicle routing problem with time windows（TDVRPTW） is considered. A multi-type ant system（MTAS） algorithm hybridized with the ant colony system（ACS）and the max-min ant system（MMAS） algorithms is proposed. This combination absorbs the merits of the two algorithms in solutions construction and optimization separately. In order to improve the efficiency of the insertion procedure, a nearest neighbor selection（NNS） mechanism, an insertion local search procedure and a local optimization procedure are specified in detail. And in order to find a balance between good scouting performance and fast convergence rate, an adaptive pheromone updating strategy is proposed in the MTAS. Computational results confirm the MTAS algorithm＇s good performance with all these strategies on classic vehicle routing problem with time windows（VRPTW） benchmark instances and the TDVRPTW instances, and some better results especially for the number of vehicles and travel times of the best solutions are obtained in comparison with the previous research.

A metaheuristic method for the multireturn-to-depot petrol truck routing problem with time windows

The petrol truck routing problem is an important part of the petrol supply chain.This study focuses on determining routes for distributing petrol products from a depot to petrol stations with the objective of minimizing the total travel cost and the fixed cost required to use the trucks.We propose a mathematical model that considers petrol trucks returning to a depot multiple times and develop a heuristic algorithm based on a local branch-and-bound search with a tabu list and the Metropolis acceptance criterion.In addition,an approach that accelerates the solution process by adding several valid inequalities is presented.In this study,the trucks are homogeneous and have two compartments,and each truck can execute at most three tasks daily.The sales company arranges the transfer amount and the time windows for each station.The performance of the proposed algorithm is evaluated by comparing its results with the optimal results.In addition,a real-world case of routing petrol trucks in Beijing is studied to demonstrate the effectiveness of the proposed approach.

The Algorithm of the Time-Dependent Shortest Path Problem with Time Windows

In this paper, we present a new algorithm of the time-dependent shortest path problem with time windows. Give a directed graph , where V is a set of nodes, E is a set of edges with a non-negative transit-time function . For each node , a time window ?within which the node may be visited and ?, is non-negative of the service and leaving time of the node. A source node s, a destination node d and a departure time?t0, the time-dependent shortest path problem with time windows asks to find an s, d-path that leaves a source node s at a departure time t0;and minimizes the total arrival time at a destination node d. This formulation generalizes the classical shortest path problem in which ce are constants. Our algorithm of the time windows gave the generalization of the ALT algorithm and A* algorithm for the classical problem according to Goldberg and Harrelson [1], Dreyfus [2] and Hart et al. [3].

The Dynamic Relationship of Brain Networks Across Time Windows During Product-Based Creative Thinking

Consensus of creativity research suggests that the measurement of both originality and valuableness is necessary when designing creativity tasks.However,few studies have emphasized valuableness when exploring underlying neural substrates of creative thinking.The present study employs product-based creativity tasks that measure both originality and valuableness in an exploration of the dynamic relationship between the default mode(DMN),executive control(ECN),and salience(SN)networks through time windows.This methodology highlights relevance,or valuableness,in creativity evaluation as opposed to divergent thinking tasks solely measuring originality.The researchers identified seven brain regions belonging to the ECN,DMN,and SN as regions of interest(ROIs),as well as four representative seeds to analyze functional connectivity in 25 college student participants.Results showed that all of the identified ROIs were involved during the creative task.The insula,precuneus,and ventrolateral prefrontal cortex(vlPFC)remained active across all stages of product-based creative thinking.Moreover,the connectivity analyses revealed varied interaction patterns of DMN,ECN,and SN at different thinking stages.The integrated findings of the whole brain,ROI,and connectivity analyses suggest a trend that the DMN and SN(which relate to bottom-up thinking)attenuate as time proceeds,whereas the vlPFC(which relates to top-down thinking)gets stronger at later stages;these findings reflect the nature of our creativity tasks and decision-making of valuableness in later stages.Based on brain region activation throughout execution of the task,we propose that product-based creative process may include three stages:exploration and association,incubation and insight,and finally,evaluation and decision making.This model provides a thinking frame for further research and classroom instruction.

MIP Formulations and Metaheuristics for Multi-Item Capacitated Lot-Sizing Problem with Non-Customer Specific Production Time Windows and Setup Times

Our research focuses on the development of two cooperative approaches for resolution of the multi-item capacitated lot-sizing problems with time windows and setup times (MICLSP-TW-ST). In this paper we combine variable neighborhood search and accurate mixed integer programming (VNS-MIP) to solve MICLSP-TW-ST. It concerns so a particularly important and difficult problem in production planning. This problem is NP-hard in the strong sense. Moreover, it is very difficult to solve with an exact method;it is for that reason we have made use of the approximate methods. We improved the variable neighborhood search (VNS) algorithm, which is efficient for solving hard combinatorial optimization problems. This problem can be viewed as an optimization problem with mixed variables (binary variables and real variables). The new VNS algorithm was tested against 540 benchmark problems. The performance of most of our approaches was satisfactory and performed better than the algorithms already proposed in the literature.

A Two-phase Solution Algorithm for the Inventory Routing Problem with Time Windows

The main objective of this paper is to propose a two-phase solution algorithm for solving the Inventory Routing Problem with Time Windows （IRPTW）, which has not been excessively researched in the literature. The solution approach is based on （a） a simple simulation for the planning phase （Phase I） and （b） the Variable Neighborhood Search Algorithm （VNS） for the routing phase （Phase II）. Testing instances are established to investigate algorithmic performance, and the computational results are then reported. The computational study underscores the importance of integrating the inventory and vehicle routing decisions. Graphical presentation formats are provided to convey meaningful insights into the problem.

MS Windows定时器性能分析及在定时-计数系统设计中的应用

通过对MSWindows定时器性能的测试和分析,利用多媒体定时器精度分布的特点,采用"合成定时-选择计数"方法获得预定精度定时,在放射测量定时-计数系统中应用。

基于ISO-TW算法的多仓储机器人路径规划研究

针对当前智能仓库中多仓储机器人协同规划效率低下、动态性不足等问题,提出了一种融合改进蛇优化(Improved Snake Optimizer,ISO)算法和时间窗(Time Window,TW)模型的路径规划方法。首先,在静态规划阶段,利用考虑阻塞因素的改进蛇优化算法为多仓储机器人规划出全局最优路径,同时计算出各机器人的时间窗,从而提升仓储机器人的规划效率;其次,在动态规划阶段,通过建立的多仓储机器人时间窗模型并引入动态调节策略,以消解冲突提升算法的动态性能;最后,进行仿真及实验。仿真结果表明,ISO-TW算法相较其他算法在阻塞点数量上最多可减少27~43个,减少幅度达22.50%~31.62%(机器人规模为80个);重规划路径比重可降低39.82%~41.05%(机器人规模为40个);平均运行时长最多可缩短38~40 s,缩短幅度约为18.27%~19.05%(机器人规模为60个)。实验结果表明,ISO-TW算法较其他算法平均运行时长减少8.53%~9.23%,冲突次数减少11.63%~15.56%,能够在真实场景中实现多仓储机器人的高效协同规划。

K-S变换及其电网超谐波时频分析应用

依据FFT→优化窗→IFFT思路,突破线性时频变换的窗函数积分性能桎梏,实现高性能优化窗函数的线性时频变换应用,建立新型时频变换算法——K-S变换.对信号x(t)的FFT频谱向量进行频移处理后,与该频移点下Kaiser优化窗的频谱向量进行Hadamard乘积,再将乘积结果进行FFT逆变换(IFFT),构造出K-S变换复时频矩阵,由此获得x(t)的时间-频率-幅值、时间-频率-相位三维信息;给出逆变换的数学推导与局部性质、线性性质和变分辨率特性;0~150 kHz电网的稳态与时变超谐波信号仿真实验表明,K-S变换的时域、频域分辨能力均优于流行的短时傅里叶变换、S变换,具有优良的变分辨率性能;0~40 kHz超谐波信号的实测证明,基于K-S变换的超谐波电压幅值测量绝对误差均小于0.032 3 V.

载人潜水器有机玻璃视窗蠕变-疲劳特性分析

通过对棒状有机玻璃试样进行短期蠕变试验,揭示载人潜水器视窗玻璃材料的蠕变特性。开展有机玻璃蠕变断裂试验,获得有机玻璃材料的蠕变断裂寿命,给出蠕变断裂时间和保载应力水平成双对数线性相关的结论及相应的线性拟合方程,并开展拉伸疲劳试验获得不同峰值应力下的拉伸疲劳寿命。对300 m观光潜器有机玻璃视窗在不同下潜深度下的应力水平进行分析,并结合基于线性累计损伤理论的时间寿命分数法,实现对300 m载人潜水器有机玻璃视窗考虑保载蠕变的疲劳寿命估算。

基于Windows CE5.0的中国-马来西亚PDA互译系统的研究

阐述了在Windows CE系统下中国--马来西亚PDA单词互译系统设计思想及开发过程,重点讲述了Windows CE的系统移植、定制和中国-马来西亚词汇互译的算法并给出了部份相关的程序代码。

动态价格约束下生鲜无人零售点选址-路径方法研究

“新零售”创新了生鲜无人零售的渠道和销售模式,通过研究网络化无人售货的技术优势,引入在线动态定价机制,解决了生鲜无人销售品控难、客户黏度低的问题,有效减低了系统综合成本;同时构建了一种面向生鲜商品的无人零售的干线和支线混杂配送模型,在多目标求解、问题解耦和PSO全局优化等方面进行突破,建立了一种动态价格约束下的带时间窗选址-路径二级运输模型(2E-dPLRPTW),并进行了案例验算,确认该方法能有效提升生鲜商品无人售卖的效益。

Windows(客户端)和MS-DOS(服务器)网络通信的实现

文中主要介绍了DOS操作系统和Windows操作系统之间网络通讯的实现方法。在Windows平台下基于VisualC++6.0环境下,利用MFC提供的Winsock类开发客户端程序。在MS-DOS平台下基于Borland C++3.1环境下,利用一个DOS下基于Packet Driver的开发包Wattcp开发服务器端程序。DOS操作系统缺乏类似Winsock的API,所以在该系统下设计基于TCP/IP协议的应用程序时,需要先安装DOS操作系统网卡的Packet Driver驱动,并在DOS下制作Make-file文件,把Wattcp所有的源程序编译、链接成库。采用C/S模式,运用Socket网络编程原理、方法和技巧,开发了基于TCP/IP协议的服务器和客户机通信程序,实现了服务器端与客户端的网络通信。

Weapon configuration, allocation and route planning with time windows for multiple unmanned combat air vehicles

Unmanned combat air vehicles(UCAVs) mission planning is a fairly complicated global optimum problem. Military attack missions often employ a fleet of UCAVs equipped with weapons to attack a set of known targets. A UCAV can carry different weapons to accomplish different combat missions. Choice of different weapons will have different effects on the final combat effectiveness. This work presents a mixed integer programming model for simultaneous weapon configuration and route planning of UCAVs, which solves the problem optimally using the IBM ILOG CPLEX optimizer for simple missions. This paper develops a heuristic algorithm to handle the medium-scale and large-scale problems. The experiments demonstrate the performance of the heuristic algorithm in solving the medium scale and large scale problems. Moreover, we give suggestions on how to select the most appropriate algorithm to solve different scale problems.

Solving Multitrip Pickup and Delivery Problem With Time Windows and Manpower Planning Using Multiobjective Algorithms

The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP(MO-MTPDPTWMP) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection(MOILS-ANS) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.