结点有约束的交通网络最短路径模型

结点有约束的网络是一类特殊的网络，如具有禁止通行限制信息的交通路网等，由于最短路径的求解是有后效性的，经典的Dijkstra算法等不能直接用来求解该问题，本文提出了一种结点有约束的交通网络最短路径建模方法，该方法所建模型为一般网络模型，可用任一传统高效的算法求其最短路径，从根本上降低了问题的复杂性，为很好地解决交通、通信等领域中的此类问题提供了有益的方法。     

工程施工中的SRTP问题研究

在大型的建设工程项目中，经常要进行场地平整工作。场地平整过程中需要进行大量的施工材料的调运工作，这引出了一个最短路径调运问题（SRTP），目标是找到一个最短的车辆行走路线，使得整个施工过程的总运输距离最短。该问题属于NP-hard问题，本文采用模拟退火算法求解该问题，最后通过箅例计算，并同贪婪算法的求解结果进行比较，验证了模拟退火算法的高效性。     

基于仿射传播的道路网络聚类

对于道路网络聚类问题,提出了仿射传播算法。首先,将道路网络上的交叉路口和结点作为顶点,建立了无向图;然后,根据最短路径计算网络距离,进而得到图的相似度矩阵;并基于仿射传播算法对道路网络进行聚类;最后,试验结果证实了本文方法的有效性与稳定性。     

一种基于Dijkstra算法的机器人避障问题路径规划

移动机器人的避障问题是移动机器人控制领域的研究热点.针对给定的移动机器人避障问题,探讨了最短路径及最短时间路径的路径规划问题.对于最短路径问题,建立了简化的路径网格模型,将其抽象为由节点及边构成的两维图,再使用经典的Dijkstra算法获得可行的最短路径.对于最短时间路径问题,通过分析移动机器人弯道运行的速度曲线,基于几何方法得出了移动时间与过渡圆弧圆心之间严格的数学关系,此后借助MATLAB优化函数获得最佳的移动路径.算法可为类似机器人避障问题的解决提供借鉴.     

网络中一类最短支撑树的计算方法

本文在无向赋权图求最短路的Dijkstra算法的基础上,提出了在有向网络图中寻找具有一个枢纽点且与其它各点均有定向联系的最短支撑树的算法,同时还给出了应用该算法的一个计算实例。     

突发事件条件下列车运行k-最短路模糊蚁群算法

突发事件造成铁路线路区间的通过能力受损,在成网条件下,铁路行车调度指挥工作客观上需要搜索列车运行k-最短路。根据突发事件的影响程度设定区间距离的事故等级系数,针对突发事件的模糊性定义了模糊隶属度函数,得到了突发事件条件下模糊区间距离;考虑列车模糊停站时分对运行径路的影响,将列车的模糊停站时分转化为广义距离;将模糊区间距离与广义距离应用到突发事件条件下铁路路网构建中,很好地处理了突发事件条件下路网信息的不确定性问题。在应用蚁群算法求解最短路径的基础上,引入了C-enough概念,将其应用于搜索突发事件条件下k-最短路径问题中。以我国部分路网为例,与传统的Dijkstra算法对比验证了模糊蚁群算法的高效性和实用性,可为列车运行调度指挥提供一定的借鉴。     

多波次导弹发射任务策略优化

针对第十四届中国研究生数学建模竞赛E题的多波次导弹发射中的规划问题展开研究.首先,简化各道路节点路径信息后通过Dijkstra算法和模拟退火算法,制定了整体最短暴露时间对应的具体发射点位及机动路线方案,得到最短暴露时间127.7h;再采用穷举法及模拟退火制定合理布设两个转载地域以及确定最优隐蔽节点的策略;最后,在考虑规避敌方打击以及单个发射装置最大暴露时间最短等其他因素下,将多目标优化问题转化为单目标优化问题.     

罚转向网络最短路径算法及其复杂性研究

本文建立和研究了具有转向惩罚值的网络模型．论文首先引入了罚转向网络符号及规则，对所建立的罚转向网络模型的性质进行了讨论，在证明了路径与子路径关系的三个定理之后，提出了求解其最短路径的算法并证明了算法的复杂性结论，论文最后给出了一个用该算法求解项转向网络的最短路径实例．     

浅析“球面距离”概念的教学

一、问题的提出 “球面距离”是立体几何中的重要概念，上海市二期课改教材（高三年级）第41页关于“球面距离”的概念是这样阐述的：“可以证明，在连接球面上两点的路径中，通过该两点的大圆劣弧最短，因此该弧的长度就是这两点的球面距离．”最近在上海市青年数学教师教学评优中，     

求最短路径树的一个新算法

本文考虑在一个具有ｎ个结点和ｍ条弧的网络中，求出从一个指定的结到其余所有结点的最短路径，或者找到一条具有负长度环路的问题，文中基于结点标号深度的概念，给出一个计算复杂性的界为Ｏ（ｎｍ）并且具有“尖利”（ｓｈａｒｐ）性质的求最短路径树的新算法。此外，我们还讨论了负长度环路的探测问题，并给出了一个具有“时间尖利”（ｔｉｍｅ－ｓｈａｒｐ）性质的检测负长度环路的方法。     

基于改进蚁群算法的最短路径问题研究及应用

蚁群系统作为一种蚁群算法是解决最短路径问题的一种行之有效的方法.然而,它自身也存在着一些缺陷,主要针对基本蚁群算法易陷入局部最优这一缺陷对其进行改进,集中体现在初始信息素求解和信息素更新这两方面.为了进一步了解改进蚁群算法的优点,进行了实验仿真:将改进的蚁群算法应用子模拟医疗救护GIS中,利用GIS的网络分析功能对城市道路网络的最短路径选择算法进行了深入地探讨研究,并以山西省太原市的交通路线作为实例进行研究.计算机仿真结果表明,改进的蚁群算法在解决最短路径问题时较基本蚁群算法的性能好,它具有一定的理论参考价值和现实意义.     

Algorithms for time-dependent bicriteria shortest path problems

In this paper we generalize the classical shortest path problem in two ways. We consider two objective functions and time-dependent data. The resulting problem, called the time-dependent bicriteria shortest path problem (TdBiSP), has several interesting practical applications, but has not gained much attention in the literature. After reviewing relevant literature we develop a new algorithm for the TdBiSP with non-negative data. Numerical tests show the superiority of our algorithm compared with an existing algorithm in the literature. Furthermore, we discuss algorithms for the TdBiSP with negative travel times and costs.     

An Algorithm for Solving the Shortest Path Improvement Problem on Rooted Trees Under Unit Hamming Distance

Shortest path problems play important roles in computer science, communication networks, and transportation networks. In a shortest path improvement problem under unit Hamming distance, an edge-weighted graph with a set of source–terminal pairs is given. The objective is to modify the weights of the edges at a minimum cost under unit Hamming distance such that the modified distances of the shortest paths between some given sources and terminals are upper bounded by the given values. As the shortest path improvement problem is NP-hard, it is meaningful to analyze the complexity of the shortest path improvement problem defined on rooted trees with one common source. We first present a preprocessing algorithm to normalize the problem. We then present the proofs of some properties of the optimal solutions to the problem. A dynamic programming algorithm is proposed for the problem, and its time complexity is analyzed. A comparison of the computational experiments of the dynamic programming algorithm and MATLAB functions shows that the algorithm is efficient although its worst-case complexity is exponential time.     

An Optimal Algorithm to Solve the All-Pairs Shortest Paths Problem on Permutation Graphs

In this paper we present an optimal algorithm to solve the all-pairs shortest path problem on permutation graphs with n vertices and m edges which runs in O(n ²) time. Using this algorithm, the average distance of a permutation graph can also be computed in O(n ²) time.     

A fuzzy shortest path with the highest reliability

This paper concentrates on a shortest path problem on a network where arc lengths (costs) are not deterministic numbers, but imprecise ones. Here, costs of the shortest path problem are fuzzy intervals with increasing membership functions, whereas the membership function of the total cost of the shortest path is a fuzzy interval with a decreasing linear membership function. By the max–min criterion suggested in [R.E. Bellman, L.A. Zade, Decision-making in a fuzzy environment, Management Science 17B (1970) 141–164], the fuzzy shortest path problem can be treated as a mixed integer nonlinear programming problem. We show that this problem can be simplified into a bi-level programming problem that is very solvable. Here, we propose an efficient algorithm, based on the parametric shortest path problem for solving the bi-level programming problem. An illustrative example is given to demonstrate our proposed algorithm.     

On a multicriteria shortest path problem

Multicriteria shortest path problems have not been treated intensively in the specialized literature, despite their potential applications. In fact, a single objective function may not be sufficient to characterize a practical problem completely. For instance, in a road network several parameters (as time, cost, distance, etc.) can be assigned to each arc. Clearly, the shortest path may be too expensive to be used. Nevertheless the decision-maker must be able to choose some solution, possibly not the best for all the criteria.In this paper we present two algorithms for this problem. One of them is an immediate generalization of the multiple labelling scheme algorithm of Hansen for the bicriteria case. Based on this algorithm, it is proved that any pair of nondominated paths can be connected by nondominated paths. This result is the support of an algorithm that can be viewed as a variant of the simplex method used in continuous linear multiobjective programming. A small example is presented for both algorithms.     

Reconstructing shortest paths

This paper develops a polynomial-time algorithm that reconstructs a shortest path between two vertices using only the all pairs shortest path distance matrix. For graphs with positive edge weights, the algorithm requiresO(⦹V|log|V|) time. When the graph contains both positive and negative, but not zero, edge weights, and all cycles have positive length, the algorithm runs inO(|V|²) time. The remarkable feature about the algorithm is that it does not require explicit information about edges in the original graph.     

Location of a semi-obnoxious facility with elliptic maximin and network minisum objectives

This paper considers the problem of locating a single semi-obnoxious facility on a general network, so as to minimize the total transportation cost between the new facility and the demand points (minisum), and at the same time to minimize the undesirable effects of the new facility by maximizing its distance from the closest population center (maximin). The two objectives employ different distance metrics to reflect reality. Since vehicles move on the transportation network, the shortest path distance is suitable for the minisum objective. For the maximin objective, however, the elliptic distance metric is used to reflect the impact of wind in the distribution of pollution. An efficient algorithm is developed to find the nondominated set of the bi-objective model and is implemented on a numerical example. A simulation experiment is provided to find the average computational complexity of the algorithm.     

带多重选择的最短路问题:复杂性和算法

本文提出了带出重选择的是短路问题,建立了该问题的数学模型,利用背包问题的一个变形问题－带限制选择的背包问题,证明了该问题是ＮＰ－Ｃ的,最后利用动态规则给出了一个伪多项式算法,其时间复杂性Ｏ（Ｃｈｍｎ）,其中ｈ是最大的选择重数。