时变条件下有宵禁限制的有害物品运输最短路研究

在有害物品运输过程中,往往需要获得从起点到终点之间的最短路.针对有害物品运输网络具有很强的时变特性,且运输过程中往往有宵禁的限制(curfews)的情况.建立了允许有多个出发时间的,时变条件下有软、硬宵禁限制的有害物品运输的最短路模型,利用动态规划设计了求解时变条件下有软、硬宵禁限制的多目标最短路的算法,通过此算法可以获得时变条件下有软、硬宵禁限制的有害物品运输最短路,并分析了算法的复杂性.然后,对网络中可行路径在不同限制条件下的目标值进行了排序,并进行了证明.最后,给出了一个应用算例,证实了算法和模型的有效性.     

一种求解时变网络下多式联运最短路的算法

在运输过程中,往往不止有一种运输方式,可能同时有多种运输方式交叉,即可能多式联运的方式存在,不同的运输方式之间需要通过转运才可实现.同时,在运输过程中,成本、运输时间、风险等因素会随着时间的不同而变化.首先,将运输网络进行变形,然后给出了在时变网络条件下多式联运的最短路模型,设计了求解时变条件下多式联运的最短路的算法,利用此算法可以获得从起点到终点之间的最短路,并对算法的计算复杂性进行了分析.最后给出一个应用算例.     

时变随机网络下基于成功和风险的应急路径选择研究

现实生活中,当发生紧急事件时,应急中心需要对某地需要服务的紧急事件出车.由于交通管理、交通流量、天气变化等因素的影响,导致了路网中各个路段上的行驶时间可能是一个与出发时间相关的随机变量.通常,对于所发生紧急时间需要在一定的应急限制期内到达.由于路网的时变随机特性,使得所选择路径可能不能完全满足应急限制期的需求.首先,定义了时变随机网络下可行应急路径中不满足应急限制的风险和满足应急限制的成功.然后,分别考虑了成功和风险两个目标,建立了时变随机网络下多目标应急路径选择模型,并设计了求解时变随机网络下应急路径选择算法,讨论了算法的计算复杂性.最后,给出了一个应用算例,并与单独考虑成功所获得的应急路径进行了对比.     

时变随机网络下有时间窗的有害物品运输路径选择研究

研究了时变随机网络下有害物品运输路径选择问题。首先定义了可行路径的具有随机性和时变性的选择向量,以期望值为目标,建立了多目标时变随机网络下有软、硬时间窗限制的有害物品运输路径选择模型。给出了时变随机网络下的有效路径的定义,并设计了多维时变随机动态标号,利用此标号设计了求解模型的多项式算法,通过此算法可以得到时变随机网络下有害物品运输路径的所有有效解。最后给出了一个应用算例。     

城市交通中车辆择路行为实证研究

随机、时变的交通流分布,偶发的交通事故等因素导致了路径随机的车辆旅行时间,也决定了现实城市交通网络中只存在随机最短路.已有的路径选择研究大都假设人们力求选择最短路,而通过实际调查发现:人们的择路行为依赖出行情景,随出行目的、约束时间、对路径的熟悉程度以及路径的不确定程度而变化.验证了城市交通中人们在不确定环境下的择路行为也符合展望理论.     

物流配送系统中大规模最短路径算法的研究

在物流配送管理系统中,车辆路径优化是一个典型的难题,而最短路径算法是其基础.传统的最短路径算法,如Dijkstra最短路径算法因性能问题无法适应大规模的拓扑网络和实时计算.本文在Dijkstra最短路径算法的基础上,在方向优先等改进算法的启发下,设计和开发了基于GIS的大规模最短路径算法.实验表明,该算法受拓扑网络规模的影响极小,能够快速完成实时最短路径计算.     

多源点突发灾害事故应急疏散模型与算法

突发灾害事故的应急疏散是减少生命财产损失,特别是减少群死群伤事故发生的有效手段.以往的研究忽视了多源点间疏散的相互影响,使得疏散线路的安排不太合理.同时考虑存在有优先顺序的多源点和容量限制情形下的应急疏散问题,建立了多源点疏散模型,设计了基于图论中网络优化思想的启发式算法.该算法引入K短路概念,并行处理多源点多线路的疏散过程,实时更新网络容量,从而得出满意的疏散线路和最短的疏散时间,并分析了算法复杂性,最后通过算例验证了该算法的有效性和可行性.     

考虑区域限制的卡车搭载无人机车辆路径问题研究

无人机参与配送是解决末端物流难题的重要途径之一。卡车搭载无人机协同配送模式,克服了无人机载重量小、续航时间短的弊端,成为无人机参与末端物流配送的重要形式之一。在疫区、灾区进行应急配送时,经常遇到由于道路毁坏或封锁、区域污染,导致部分路段车辆或无人机无法通行的情况。在非应急配送中,也可能存在车辆限行和空域禁飞等区域限制措施。区域限制给卡车搭载无人机物流配送路径优化问题带来了很大挑战。论文构建了区域限制条件下卡车搭载无人机车辆路径问题的混合整数线性规划模型,提出了一种结合最短路算法和禁忌搜索算法的混合算法,基于标准算例库设计测试集并进行测试实验,实验结果表明混合算法具有较好的计算性能。     

实时/时变路网环境下城市出救点选择与救援车辆路径的集成动态优化

为了提升城市突发公共事件应急资源调配效率,考虑实时/时变路网环境下出救点选择与救援车辆路径的集成优化问题(CERFSVRP),设计了一种实时/时变交通信息的结合策略,并提出了满足先进先出原则的路段行驶时间计算方法.在此基础上,综合考虑出救点选择、供应能力以及车辆路径连续性等约束条件,以应急响应时间最短为目标,基于虚拟出救点概念和滚动时域策略建立了CERFSVRP动态优化模型.针对该模型的特点,设计了一种改进遗传算法和线性规划法相结合的两阶段算法.算例分析结果表明该模型和算法可以有效解决实时/时变路网环境下城市突发公共事件CERFSVRP动态优化问题.     

网络弧生长对网络可靠性的影响

人们普遍认为,对于一般的网络图而言,当网络弧在生长时将有益于网络可靠性的增强。然而事实证明该论断并不完全正确。对于某些注重最短路径长度的网络而言:(1) 当网络>G受到随机攻击时,以网络最短路熵作为网络可靠性的判断依据,分别计算在增加一条弧前后网络结点或网络弧受到攻击而失效时的网络最短路熵,比较网络最短路熵的大小,熵值越大,可靠性越弱;(2) 当网络G受到恶意攻击时,根据悲观原则定义了一种新的可靠性指标,该指标值越大,网络可靠性越强。结果表明:(1) 当G与G'的最短路径长度相等时,G'的可靠性不弱于G;(2)当G与G'的最短路径长度不相等时,G与G'的可靠性相对大小关系不唯一。对该问题的研究有助于人们清楚的认识到网络弧生长对网络可靠性的影响。最后用简化的江苏省城市间高速公路网络图为例来说明该研究的有效性与实用性。     

Technology adoption and training practices as a constrained shortest path problem

Technology adoption is not a new venue for research. Much work of decision modeling, diffusion of new technology and statistical analysis of survey data has been done. Some studies focus on finding the optimal forms of technology to adopt within a complementarity framework, but there is no mention of finding an optimal path from a firm's current state to its optimal state. This represents a significant gap in the literature. The paper applies a constrained shortest path problem to training and technology adoption decisions by firms. Given the current set of training and technology adoption the method solves for what technology/practice should be adopted or removed from the complete set of combinations and in what order so as to maximize performance subject to budget constraints. To the authors' knowledge, this is the first application of the constrained shortest path problem to technology adoption decisions.     

Reliable Ad Hoc Routing Based on Mobility Prediction

Reliability is a very important issue in Mobile Ad hoc NETworks (MANETs). Shortest paths are usually used to route packets in MANETs. However, a shortest path may fail quickly, because some of the wireless links along a shortest path may be broken shortly after the path is established due to mobility of mobile nodes. Rediscovering routes may result in substantial data loss and message exchange overhead. In this paper, we study reliable ad hoc routing in the urban environment. Specifically, we formulate and study two optimization problems. In the minimum Cost Duration-bounded Path (CDP) routing problem, we seek a minimum cost source to destination path with duration no less than a given threshold. In the maximum Duration Cost-bounded Path (DCP) routing problem, we seek a maximum duration source to destination path with cost no greater than a given threshold. We use a waypoint graph to model the working area of a MANET and present an offline algorithm to compute a duration prediction table for the given waypoint graph. An entry in the duration prediction table contains the guaranteed worst-case duration of the corresponding wireless link. We then present an efficient algorithm which computes a minimum cost duration-bounded path, using the information provided in the duration prediction table. We also present a heuristic algorithm for the DCP routing problem. In addition, we show that the proposed prediction and routing schemes can be easily applied for designing reliable ad hoc routing protocols. Simulation results show that our mobility prediction based routing algorithms lead to higher network throughput and longer average path duration, compared with the shortest path routing. This research was supported in part by ARO grant W911NF-04-1-0385 and NSF grant CCF-0431167. The information reported here does not reflect the position or the policy of the federal government.     

Assembly Line Balancing with Resource Dependent Task Times*

Paced assembly lines are widely used in repetitive manufacturing applications. Most previous research on the design of paced lines has assumed that each task along the line can be performed by only one worker (or a fixed number of workers). In many cases, however, task duration times may be reduced by increasing the number of workers or changing the equipment assigned to work stations. Thus, the problem becomes one of assigning resource alternatives (e.g., workers and/or equipment) and tasks to work stations to minimize total cost for a desired production rate. For this problem, we present three procedures. The first formulates the problem as a shortest path problem and guarantees optimality. The second and third are heuristic adaptations of the shortest path procedure that are capable of solving large problems. The procedures are compared in terms of solution quality and computation time on a set of 128 randomly generated problems for which optimal solutions could be found. Our simulation results indicate that the choice of procedure depends on problem complexity and resource costs.     

A Genetic Algorithm for the Weight Setting Problem in OSPF Routing

With the growth of the Internet, Internet Service Providers (ISPs) try to meet the increasing traffic demand with new technology and improved utilization of existing resources. Routing of data packets can affect network utilization. Packets are sent along network paths from source to destination following a protocol. Open Shortest Path First (OSPF) is the most commonly used intra-domain Internet routing protocol (IRP). Traffic flow is routed along shortest paths, splitting flow at nodes with several outgoing links on a shortest path to the destination IP address. Link weights are assigned by the network operator. A path length is the sum of the weights of the links in the path. The OSPF weight setting (OSPFWS) problem seeks a set of weights that optimizes network performance. We study the problem of optimizing OSPF weights, given a set of projected demands, with the objective of minimizing network congestion. The weight assignment problem is NP-hard. We present a genetic algorithm (GA) to solve the OSPFWS problem. We compare our results with the best known and commonly used heuristics for OSPF weight setting, as well as with a lower bound of the optimal multi-commodity flow routing, which is a linear programming relaxation of the OSPFWS problem. Computational experiments are made on the AT&T Worldnet backbone with projected demands, and on twelve instances of synthetic networks.     

Computing an $$L_1$$ shortest path among splinegonal obstacles in the plane

Journal of Combinatorial Optimization - We reduce the problem of computing an $$L_1$$ shortest path between two given points s and t in the given splinegonal domain $$\mathcal {S}$$ to the problem...     

Improvement of path analysis algorithm in social networks based on HBase

When social network has reached hundreds of million users, the analysis of data in social network services becomes very important. Understanding how nodes interconnect in large graphs is an essential problem in many fields. In order to find connecting nodes between two nodes or two groups of source nodes in huge graphs, we propose a parallelized data-mining algorithm to get the shortest path between nodes in a social network based on HBase distributed key/value store. Our algorithm can achieve the shortest path among different nodes in network under the parallel environment. We analyze the social network model by this algorithm first, and then optimize the output from cloud platform by using the intermediary degrees and degree central algorithm. Finally, with a simulated social network, we validate the efficiency of the proposed algorithm. The experiment results indicate that our algorithm can improve the efficiency of parallel breath-first search (BSF).     

Solution Structure of Some Inverse Combinatorial Optimization Problems

In this paper we consider some inverse combinatorial optimization problems, that is, for a given set of feasible solutions of an optimization problem, we wish to know: under what weight vectors (or capacity vectors) will these feasible solutions become optimal solutions? We analysed shortest path problem, minimum cut problem, minimum spanning tree problem and maximum-weight matching problem, and found that in each of these cases, the solution set of the inverse problem is charaterized by solving another combinatorial optimization problem. The main tool in our approach is Fulkerson's theory of blocking and anti-blocking polyhedra with some necessary revisions.     

Efficient Benders decomposition for distance-based critical node detection problem

This paper addresses the critical node detection problem which seeks a subset of nodes for removal in order to maximize the disconnectivity of the residual graph with respect to a specific distance-based measure, namely the Wiener index. Such a measure is defined based on the all-pair shortest path distances in the residual graph so that the longer the total length of shortest paths, the greater the value of the disconnectivity measure. In the literature, a mixed integer linear programming model and an exact iterative-based method have been presented for this problem; however, both approaches become very time-consuming on graphs having large diameter and non-unit edge lengths. To overcome this shortcoming, in this paper, we present a new formulation for the problem and solve it by Benders decomposition algorithm. We improve the performance of Benders algorithm by several techniques (including analytical calculation of dual variables, generation of good-quality initial optimality cuts, considering master's optimality cuts as lazy constraints, etc.) to reduce the total running time. The extensive computational experiments on instances, taken from the literature or generated randomly, confirm the effectiveness of the new approaches.