树网络上f模式最优广播问题的线性算法

网络G的一个结点v上的一次广播是指从它将一个消息传递给若干相邻结点.所谓f模式广播,是指结点v在一次广播中至多向f(v)个相邻结点传递信息(f为给定的整值函数).假定每一次广播的执行时间为一单位.网络G的广播过程是广播的时间安排,使所有结点均获得消息.最优广播问题是求总时间最少的广播过程.在G是树网络情形,文献中已给出时间界为O(n2)的算法.本文给出线性时间的简捷算法.     

带运输机的流水车间调度的最优算法

研究带运输时间的流水调度:在该问题中有两台机器A,B和一个运输机V,n个工件,工件需要先在机器A上加工然后在机器B上加工最后被运输机V运往目的地,而且运输机V最初停在机器B旁边.模型的目标是使所有工件都运往目的地的时间最短.文中给出了三种情况下的最优调度算法:i)A,B机器加工工件顺序给定时我们给出了线性时间的最优算法;ii)所有的工件加工时间在机器B上时间相等时我们给出了时间复杂度为O(nlogn)的最优算法;iii)机器B上工件最短加工时间大于等于机器A上工件最长加工时间时给出了时间复杂度为O(n~2)的最优算法.     

基于数据和整体的航空网络静态定价模型

建立了一个静态定价与座位分配联合模型.利用模型的性质,将问题简化为一个可分的凹规划模型.特别地,在一个三航段网络上,从模型的网络流形式推出最优目标函数具有良好的性质,并对模型的最优价格决策进行了灵敏度分析.最后给出了一个双枢纽网络上对细分产品定价的算例.     

一类随机绝对超前-延误惩罚单机排序

研究在一台随机发生故障的机器上加工n个具有同一工期的工件, 使得所谓绝对超前-延误惩罚的数学期望最小的调度问题.详细地讲, 问题中的目标测度是最小化完工时间与公共 工期之绝对偏差和的数学期望. 我们在机器的工作时间服从指数分布的条件下分中断-恢复型问题和中断-重复型问题进行研究(对于中断-重复型要求故障时间服从指数分布或是一 个常数). 主要工作如下: (1)问题规划和预备知识. 建立支持后续工作的定义,关系和事实. 特别地, 证明了一个加工时间为t的工件的完工时间与任一工期之绝对偏差的数学期望是关于变量t的半V型函数; (2) 最优解的性质.给出了最优解的几个特征.最重要的是, 证明了最优解具有半V型性质; (3)算法.讨论了几个关于求所研究问题最优解的计算问题.     

Polish空间上的折扣马氏过程量子化策略的渐近优化北大核心CSCD

该文研究了Polish空间上、带折扣因子的连续时间马尔可夫决策过程(CTMDPs)的量子化平稳策略的渐近最优性问题.首先,建立了折扣最优方程(DOE)及其解的存在性和唯一性.其次,在适当的条件下证明了最优确定性平稳策略的存在性.此外,为了对行动空间进行离散化,构造了一列量子化策略,利用有限行动空间的策略来逼近一般(Polish)空间上的折扣CTMDPs最优平稳策略.最后,通过一个例子来说明该文的渐近逼近结果.     

一类跳扩散需求存贮系统(s,S)库存控制策略研究

考虑的是连续检查库存,需求为一个常时间函数和-个复合Poison跳扩散随机过程的和的存贮系统最优库存控制问题.基于期望折扣成本最小建立了无穷时间区间具有固定订购成本的最优库存模型,确定可采用(s,S)策略进行库存控制,给出了最优(s,S)策略的充要条件--HJB方程Ⅰ、Ⅱ.我们采用"猜测"的方法确定了最优(s,S)策略对应的值函数形式,建立了确定库存参数的最优化模型.     

一个约束乘积最大问题的强多项式时间算法

本文讨论了约束乘积最大问题最优解的结构特征,在此基础上给出了一个计算时间为O(n2)的强多项式时间算法,并且对于单边约束的情形给出了复杂度更低(O(nlnn))的强多项式时间算法.     

基于道路堵塞及顾客时间要求的快件配送最优路径选择

针对道路堵塞如节假日导致的临时最短配送路径失效的问题,提出配送网络最优路径选择模型,并设计了求解快递配送网络关键边和最优路径的算法。首先,计算出整个网络的关键边,掌握配送网络特征;其次,考虑顾客时间要求,研究不完全信息(中断无法提前预知,只有到达中断边的起点处才可知)下的最优路径,根据最短路径上各边新的特点,计算出每条边中断后对应的一组备用路径,再选择运输时间小于或等于顾客可等待时间的路径为有效路径,考虑道路堵塞情况,从有效路径中选择最优路径;最后,结合配送网络的实际情况对最优路径进行了算例分析。     

基于航段需求计算网络竞价的近似动态规划方法

竞价控制是收益管理中广泛应用的一种存量控制方法.将网络存量控制问题描述为一个动态规划模型,通过状态向量的一个仿射函数近似动态规划的最优值函数,并且在航段水平上考虑随机需求,最终得到一个计算网络竞价所需的确定性线性规划(DLP),相对于标准的DLP,这个DLP得到了更接近于动态规划最优值的上界.给出了一个列生成算法用于求解这个DLP,并提供了模拟算例,计算结果表明可获得比标准的DLP方法更好的收益.     

(m,n,k)指派问题的最小费用流模型及其算法

构造(m,n,k)指派问题的最小费用流模型,并将基于对偶原理的最小费用流的允许边算法求解该模型,提出求解(m,n,k)指派问题的一种算法.算法直接在其对应的网络中保持互补松弛条件不变,通过调整节点势以扩大允许网络从而寻求增广链并进行流量增广,直至在网络中得到流量为k的最小费用流,此时非O流边对应(m,n,k)指派问题的最优解.给出了(m,n,k)指派问题的最优解及多重最优解的重要性质,数值试验表明算法有效可行.     

图的最小控制树问题（英文）

A dominating tree T of a graph G is a subtree of G which contains at least one neighbor of each vertex of G.The minimum dominating tree problem is to find a dominating tree of G with minimum number of vertices,which is an NP-hard problem.This paper studies some polynomially solvable cases,including interval graphs,Halin graphs,special outer-planar graphs and others.     

次最小树及其相关问题-算法设计和复杂性分析

支撑树问题已经有很长的研究历史了,见[1].在许多工程问题中,需要产生一个网络G的所有支撑树,见[2,3,4].当G为赋权图时,每棵支撑树T有长度L（T）.在产生G的所有支撑树时,许多工程问题希望按照L（T）的非降顺序产生,见[5,6].在按照L（T）的非降顺序产生的支撑树中,有许多支撑树长度是相同的,而支撑树的数目又非常大（可以高达nn-2个）,因此算法的计算量非常大.本文希望能够按照L（T）的严格上升顺序产生所有的支撑树,从而避免大量的重复计算.     

How to Pack Trees

In a virtual memory system, the address space is partitioned into pages, and the main memory serves as a cache to the disk. In this setting, we address the following problem: Given a tree, find an allocation of its nodes to pages, so-called a packing, which optimizes the cache performance for some access pattern to the tree nodes. We investigate a model for tree access in which a node is accessed only via the path leading to it from the root. Two cost functions are considered: the total number of different pages visited in the search, and the number of page faults incurred. It is shown that both functions can be optimized simultaneously. An efficient dynamic programming algorithm to find an optimal packing is presented. The problem of finding an optimal packing which also uses the minimum number of pages is shown to be NP-complete. However, an efficient approximation algorithm is presented. This algorithm finds a packing that uses the minimum number of pages and requires at most one extra page fault per search. Finally, we study this problem in the context of dynamic trees which allow insertions and deletions.     

Variable neighbourhood search for the minimum labelling Steiner tree problem

We present a study on heuristic solution approaches to the minimum labelling Steiner tree problem, an NP-hard graph problem related to the minimum labelling spanning tree problem. Given an undirected labelled connected graph, the aim is to find a spanning tree covering a given subset of nodes of the graph, whose edges have the smallest number of distinct labels. Such a model may be used to represent many real world problems in telecommunications and multimodal transportation networks. Several metaheuristics are proposed and evaluated. The approaches are compared to the widely adopted Pilot Method and it is shown that the Variable Neighbourhood Search that we propose is the most effective metaheuristic for the problem, obtaining high quality solutions in short computational running times.     

A Subdivision Approach to Maximum Parsimony

Determining an optimal phylogenetic tree using maximum parsimony, also referred to as the Steiner tree problem in phylogenetics, is NP hard. Here we provide a new formulation for this problem which leads to an analytical and linear time solution when the dimensionality (sequence length, or number of characters) is at most two. This new formulation of the problem provides a direct link between the maximum parsimony problem and the maximum compatibility problem via the intersection graph. The solution for the “two character case” has numerous practical applications in phylogenetics, some of which are discussed. Received January 16, 2006     

On Minimum Edge Ranking Spanning Trees

In this paper, we introduce the problem of computing a minimum edge ranking spanning tree (MERST); i.e., find a spanning tree of a given graph G whose edge ranking is minimum. Although the minimum edge ranking of a given tree can be computed in polynomial time, we show that problem MERST is NP-hard. Furthermore, we present an approximation algorithm for MERST, which realizes its worst case performance ratio where n is the number of vertices in G and Δ* is the maximum degree of a spanning tree whose maximum degree is minimum. Although the approximation algorithm is a combination of two existing algorithms for the restricted spanning tree problem and for the minimum edge ranking problem of trees, the analysis is based on novel properties of the edge ranking of trees.     

Greedy Randomized Adaptive Search and Variable Neighbourhood Search for the minimum labelling spanning tree problem

This paper studies heuristics for the minimum labelling spanning tree (MLST) problem. The purpose is to find a spanning tree using edges that are as similar as possible. Given an undirected labelled connected graph, the minimum labelling spanning tree problem seeks a spanning tree whose edges have the smallest number of distinct labels. This problem has been shown to be NP-hard. A Greedy Randomized Adaptive Search Procedure (GRASP) and a Variable Neighbourhood Search (VNS) are proposed in this paper. They are compared with other algorithms recommended in the literature: the Modified Genetic Algorithm and the Pilot Method. Nonparametric statistical tests show that the heuristics based on GRASP and VNS outperform the other algorithms tested. Furthermore, a comparison with the results provided by an exact approach shows that we may quickly obtain optimal or near-optimal solutions with the proposed heuristics.     

Tree search for the stacking problem

The stacking problem is a hard combinatorial optimization problem with high practical interest in, for example, steel storage or container port operations. In this problem, a set of items is stored in a warehouse for a period of time, and a crane is used to place them in a limited number of stacks. Since the entrance and exit of items occurs in an arbitrary order, items may have to be relocated in order to reach and deliver other items below them. The objective of the problem is to find a feasible sequence of movements that delivers all items, while minimizing the total number of movements. We study the scalability of an exact approach to this problem, and propose two heuristic methods to solve it approximately. The two heuristic approaches are a multiple simulation algorithm using semi-greedy construction heuristics, and a stochastic best-first tree search algorithm. The two methods are compared in a set of challenging instances, revealing a superior performance of the tree search approach in most cases.     

Efficient algorithms for wavelength assignment on trees of rings

A fundamental problem in communication networks is wavelength assignment (WA): given a set of routing paths on a network, assign a wavelength to each path such that the paths with the same wavelength are edge-disjoint, using the minimum number of wavelengths. The WA problem is NP-hard for a tree of rings network which is well used in practice. In this paper, we give an efficient algorithm which solves the WA problem on a tree of rings with an arbitrary (node) degree using at most 3L wavelengths and achieves an approximation ratio of 2.75 asymptotically, where L is the maximum number of paths on any link in the network. The 3L upper bound is tight since there are instances of the WA problem that require 3L wavelengths even on a tree of rings with degree four. We also give a 3L and 2-approximation (resp. 2.5-approximation) algorithm for the WA problem on a tree of rings with degree at most six (resp. eight). Previous results include: 4L (resp. 3L) wavelengths for trees of rings with arbitrary degrees (resp. degree at most eight), and 2-approximation (resp. 2.5-approximation) algorithm for trees of rings with degree four (resp. six).     

Comparison of Algorithms for the Degree Constrained Minimum Spanning Tree

The Degree Constrained Minimum Spanning Tree (DCMST) on a graph is the problem of generating a minimum spanning tree with constraints on the number of arcs that can be incident to vertices of the graph. In this paper we develop three heuristics for the DCMST, including simulated annealing, a genetic algorithm and a method based on problem space search. We propose alternative tree representations to facilitate the neighbourhood searches for the genetic algorithm. The tree representation that we use for the genetic algorithm can be generalised to other tree optimisation problems as well. We compare the computational performance of all of these approaches against the performance of an exact solution approach in the literature. In addition, we also develop a new exact solution approach based on the combinatorial structure of the problem. We test all of these approaches using standard problems taken from the literature and some new test problems that we generate.