基于人工蜂群算法的配送中心选址问题求解

采用人工蜂群算法对配送中心选址问题进行求解,给出食物源的编码方法,通过整数规范化,使算法能在整数空间内对问题进行求解.应用算法进行了仿真实验,并将结果与其它一些启发式算法进行了比较和分析.计算结果表明人工蜂群算法可以有效求解配送中心选址问题,同时也为算法求解其它一些组合优化问题提供了有益思路.     

多时间窗车辆路径问题的智能水滴算法

研究了多时间窗车辆路径问题,考虑了车容量、多个硬时间窗限制等约束条件,以动用车辆的固定成本和车辆运行成本之和最小为目标,建立了整数线性规划模型。根据智能水滴算法的基本原理,设计了求解多时间窗车辆路径问题的快速算法,利用具体实例进行了模拟计算,并与遗传算法的计算结果进行了对比分析,结果显示,利用智能水滴算法求解多时间窗车辆路径问题,能够以很高的概率得到全局最优解,是求解多时间窗车辆路径问题的有效算法。     

小样本球面地面条件下的三维无源定位算法

主要考虑移动目标的小样本定位概率方法。给定侦查机的经纬度和飞行高度随时间的变化序列、到达角信息(到达方向角/到达俯仰角)的条件下,利用雷达通信过程空间球面地面几何关系和等概率曲线理论,研究无源定位侦查目标的经纬度和高度随时间变化的关系,同时给出了算法定位误差的计算方法。由于传统平坦地面无源定位算法没有考虑地球表面的曲率,本文给出的无源定位算法充分考虑了地面曲率对定位精度的影响。理论证明,传统的无源定位算法是本文算法的一阶近似。仿真实验验证了所提算法的正确性和有效性,同时表明本文算法比传统定位算法精度高。     

基于相异度的SVM选择性集成雾霾天气预测方法

目前雾霾污染日益严重,威胁到了环境保护和人类健康,需要对雾霾天气进行预测.通过对多个支持向量机(SVM)进行选择性集成,克服单个SVM不稳定的缺点,提出了基于相异度的SVM选择性集成雾霾天气预测方法(DSE-SVM).首先采用高斯核SVM独立训练出多个个体SVM;其次计算出个体SVM的相异度,剔除相异度最大的个体SVM;最后运用多数投票算法对剩余的SVM进行集成,并进行了理论分析.通过对北京、上海和广州三地区近两年的雾霾数据进行实验分析,实验结果表明DSE-SVM方法预测性能更优,具有较高的稳定性和可信性.     

一种基于遗传禁忌昆合策略的时延约束组播路由算法

提出了一种基于遗传算法和禁忌搜索法相结合混合策略的时延约束最小代价组播路由算法(GATSA).该算法利用Djjkstra第k最短路径算法找出源节点到每一个目的节点满足最大时延限制的路径,通过遗传禁忌混合策略的选择、交叉与变异等操作,求出满足条件的组播树.仿真实验结果表明本算法性能和算法性能稳定,其代价性能接近目前性能最好的BSMA算法,并具有快速,低时延的特.     

非对称相异性矩阵的一种非度量多维尺度变换

本文将Okada ＆ Imaizumi等的模型加以推广,提出了一种用于处理非对称相异性矩阵的非度量多维尺度变换新方法.在模型中,我们假定每个研究对象可以表示为Minkowski度量空间中的一个点和一个超球面,超球面的半径揭示了相应研究对象的非对称性.文中我们给出了一种计算点坐标及球半径的算法.该算法使用了代数方法,比原来的方法收敛速度快,节省计算时间.最后给出了一个数值例子.     

考虑车载容量的多区型仓库拣货路径优化研究

拣货作业是仓库核心作业之一,占据仓库运营大量的时间成本和资金成本.针对多区型仓库拣货路径优化问题,对多区型仓库布局、货位坐标、路径等问题进行了定义,构建了多区型仓库拣货路径优化建模,接着通过大量实验确定了人工鱼群算法在求解拣货路径问题时的最优算法参数组合,通过演示性实验验证了模型与算法的有效性,最后从波次订单对实验结果...     

求解带时间窗车辆路径问题的混合Memetic算法

为提高带时间窗车辆路径问题的求解精度和求解效率,设计了一种混合Memetic算法。采用基于时间窗升序排列的混合插入法构造初始种群,提高解质量的同时兼顾多样性,扩大搜索空间;任意选择组成父代种群,以维持搜索空间;运用简化的变邻域搜索进行局部开发,引入邻域半径减少策略提高开发效率,约束放松机制开放局部空间;以弧为对象,增加种群向当前最优解和全局最优解的后学习过程。实验结果表明,所提出的算法具有较好的寻优精度和稳定性,能搜索到更好的路径长度结果,更新了现有研究在最短路径长度的目标函数上的下限。     

一类含有参数和有理奇性哈密顿系统的同宿与异宿轨道

研究一类含有两个参数和有理奇性平面哈密顿系统的同宿与异宿轨道,该问题来源于一个关于聚合物流体剪切流动特性的研究．借助常微定性理论和不变流形分析的方法,文中给出了系统存在同宿与异宿轨道的条件,并通过数值计算检验了所得理论结果。     

基于交通流的多模糊时间窗车辆路径优化

研究了基于交通流的多模糊时间窗车辆路径问题,考虑了实际中不断变化的交通流以及客户具有多个模糊时间窗的情况,以最小化配送总成本和最大化客户满意度为目标,构建基于交通流的多模糊时间窗车辆路径模型。根据伊藤算法的基本原理,设计了求解该模型的改进伊藤算法,结合仿真算例进行了模拟计算,并与蚁群算法的计算结果进行了对比分析,结果表明,利用改进伊藤算法求解基于交通流的多模糊时间窗车辆路径问题,迭代次数小,效率更高,能够在较短的时间内收敛到全局最优解,可以有效的求解多模糊时间窗车辆路径问题。     

供给总量限定需求区间约束型运输问题——时限费用优化模型与算法

本文目的是为建立与运输问题有关的决策支持系统提供方便.本文建立了供给总量限定需求区间约束型运输问题的对时限与费用两个目标进行优化的多目标规划模型,给出了求解模型的算法,并举例说明了算法的应用.该算法能求得问题的最优解,并具有易于编程实现、收敛性好等优点.数值实验表明该算法有较高的计算效率,可用于求解某些类型的指派问题.     

Realistic two-stage flowshop batch scheduling problems with transportation capacity and times

This paper investigates single-batch and batch-single flow shop scheduling problem taking transportation among machines into account. Both transportation capacity and transportation times are explicitly considered. While the single processing machine processes one job at a time, the batch processing machine processes a batch of jobs simultaneously. The batch processing time is the longest processing times of jobs assigned to that batch.Each problem is formulated as a mixed integer programming model to find optimal makespan. Lower bounds and heuristic algorithms are proposed and computational experiments are carried out to verify their effectiveness.     

Joint optimization of budget allocation and maintenance planning of multi-facility transportation infrastructure systems

Transportation infrastructure, such as pavements and bridges, is critical to a nation’s economy. However, a large number of transportation infrastructure is underperforming and structurally deficient and must be repaired or reconstructed. Maintenance of deteriorating transportation infrastructure often requires multiple types/levels of actions with complex effects. Maintenance management becomes more intriguing when considering facilities at the network level, which represents more challenges on modeling interdependencies among various facilities. This research considers an integrated budget allocation and preventive maintenance optimization problem for multi-facility deteriorating transportation infrastructure systems. We first develop a general integer programming formulation for this problem. In order to solve large-scale problems, we reformulate the problem and decompose it into multiple Markov decision process models. A priority-based two-stage method is developed to find optimal maintenance decisions. Computational studies are conducted to evaluate the performance of the proposed algorithms. Our results show that the proposed algorithms are efficient and effective in finding satisfactory maintenance decisions for multi-facility systems. We also investigate the properties of the optimal maintenance decisions and make several important observations, which provide helpful decision guidance for real-world problems.     

Algorithms for the Network Oligopoly Problem

Network oligopoly models have been recently proposed as an alternative to general equilibrium models for analysing conflict situations between spatially separated firms. However, the oligopoly models are, from an algorithmic standpoint, less well understood than their equilibrium counterparts. In this paper we propose four iterative schemes for computing Cournot-Nash equilibria on congested transportation networks. The algorithms will be analysed theoretically, and computational results will be provided (i) for an oligopoly test problem, and (ii) for a small-scale network oligopoly problem.     

Scheduling flexible job-shops with transportation times: Mathematical models and a hybrid imperialist competitive algorithm

After the completion of a job on a machine, it needs to be transported to the next machine, actually taking some time. However, the transportation times are commonly neglected in the literature. This paper incorporates the transportation times between the machines into the flexible job-shop scheduling problem. We mathematically formulate the problem by two mixed integer linear programming models. Since the problem is NP-hard, we propose an adaptation of the imperialist competitive algorithm hybridized by a simulated annealing-based local search to solve the problem. Various operators and parameters of the algorithm are calibrated using the Taguchi method. The presented algorithm is assessed by comparing it against two other competitive algorithms in the literature. The computational results show that this algorithm has an outstanding performance in solving the problem.     

Combinatoric classes of the transportation problem and their properties

This paper considers the partitioning of all transportation problem instances into a finite number of equivalent classes. Combinatoric properties of each class, in both primal and dual spaces, are investigated. The problem instances are partitioned into convex regions by degeneracy hyperplanes. Properties of adjacent regions are fully developed. It turns out that adjacency relations are much more complex in the primal space than in the dual space. Finally, algorithms based on the simplex method are classified according to how they behave in each class.     

具有两种运输方式选择的物流排序问题

本文研究了单机环境下,有两种运输方式可供选择的集成生产和运输的排序问题。有多个工件需要在一台机器上进行加工,工件生产完后需要分批运到客户处。有两种运输方式,普通运输和特快运输可供选择。制造商需要安排工件的加工顺序,选择合适的运输方式和出发时间,以极小化相应的时间目标与运输费用的加权和。研究了排序理论中主要的两个目标函数,分析了问题的复杂性,对于这些问题给出了它们的最优算法。     

Complexities and algorithms for synchronized scheduling of parallel machine assembly and air transportation in consumer electronics supply chain

In this paper, we study the problem of synchronized scheduling of assembly and air transportation to achieve accurate delivery with minimized cost in consumer electronics supply chain. This problem was motivated by a major PC manufacturer in consumer electronics industry. The overall problem is decomposed into two sub-problems, which consist of an air transportation allocation problem and an assembly scheduling problem. The air transportation allocation problem is formulated as an integer linear programming problem with the objective of minimizing transportation cost and delivery earliness tardiness penalties. The assembly scheduling problem seeks to determine a schedule ensuring that the orders are completed on time and catch the flights such that the waiting penalties between assembly and transportation is minimized. The problem is formulated as a parallel machine scheduling problem with earliness penalties. The computational complexities of the two sub-problems are investigated. The air transportation allocation problem with split delivery is shown to be solvable. The parallel machine assembly scheduling problem is shown to be NP-complete. Simulated annealing based heuristic algorithms are presented to solve the parallel machine problem.     

一种新的交通网络设计优化算法

交通网络设计问题是研究如何用定量的方法在已有交通网络上添加或扩容某些路段的问题.文章在回顾交通网络设计问题文献的基础上,提出了基于图论网络优化思想的解决该类问题的一种新思路,给出了启发式算法,并进行了算法复杂性分析,最后通过算例验证了其有效性.     

Filtering Problem for Discrete Volterra Equations with Combined Disturbances

Abstract

A minimax filtering problem for discrete Volterra equations with combined noise models is considered. The combined models are defined as the sums of uncertain bounded deterministic functions and stochastic white noises. However, the corresponding variational problem turns out to be very difficult for direct solution. Therefore, simplified filtering algorithms are developed. The levels of nonoptimality for these simplified algorithms are introduced as the ratios of the filtering performances for the simplified and optimal estimators.

In opposite to the original variational problem, these levels can be easily evaluated numerically. Thus, simple filtering algorithms with guaranteed performance are obtained. Numerical experiments confirm the efficiency of our approach.