A tabu search approach for scheduling hazmat shipments

Vehicle routing and scheduling are two main issues in the hazardous material (hazmat) transportation problem. In this paper, we study the problem of managing a set of hazmat transportation requests in terms of hazmat shipment route selection and actual departure time definition. For each hazmat shipment, a set of minimum and equitable risk alternative routes from origin to destination points and a preferred departure time are given. The aim is to assign a route to each hazmat shipment and schedule these shipments on the assigned routes in order to minimize the total shipment delay, while equitably spreading the risk spatially and preventing the risk induced by vehicles traveling too close to each other. We model this hazmat shipment scheduling problem as a job-shop scheduling problem with alternative routes. No-wait constraints arise in the scheduling model as well, since, supposing that no safe area is available, when a hazmat vehicle starts traveling from the given origin it cannot stop until it arrives at the given destination. A tabu search algorithm is proposed for the problem, which is experimentally evaluated on a set of realistic test problems over a regional area, evaluating the provided solutions also with respect to the total route risk and length.     

A comprehensive modeling framework for hazmat network design,hazmat response team location,and equity of risk

This paper considers a bi-level hazmat transportation network design problem in which hazmat shipments have to be transported over a road network between specified origin-destination points. The bi-level framework involves a regulatory authority and hazmat carriers. The control variables for the regulatory authority are locations of hazmat response teams and which additional links to include for hazmat travel. The regulatory authority (upper level) aims to minimize the maximum transport risk incurred by a transportation zone, which is related to risk equity. Our measure of risk incorporates the average response time to the hazmat incidents. Hazmat carriers (lower level) seek to minimize their travel cost. Using optimality conditions, we reformulate the non-linear bi-level model as a single-level mixed integer linear program, which is computationally tractable for medium size problems using a commercial solver. For large size problems, we propose a greedy heuristic approach, which we empirically demonstrate to find good solutions with reasonable computational effort. We also seek a robust solution to capture stochastic characteristics of the model. Experimental results are based on popular test networks from the Sioux Falls and Albany areas.     

Robust Facility Location Problem for Hazardous Waste Transportation

We consider a robust facility location problem for hazardous materials (hazmat) transportation considering routing decisions of hazmat carriers. Given a network and a known set of nodes from which hazmat originate, we compute the locations of hazmat processing sites (e.g. incinerators) which will minimize total cost, in terms of fixed facility cost, transportation cost, and exposure risk. We assume that hazmat will be taken to the closest existing processing site. We present an exact full enumeration method, which is useful for small or medium-size problems. For larger problems, the use of a genetic algorithm is explored. Through numerical experiments, we discuss the impact of uncertainty and robust optimization in the hazmat combined location-routing problem.     

Influence of Social Networks on Latent Choice of Electric Cars: A Mixed Logit Specification Using Experimental Design Data

Electric cars can potentially make a substantial contribution to the reduction of pollution and noise. The size of this contribution depends on the acceptance of this new technology in the market. This paper reports on the design and results of an elaborate stated choice experiment to investigate the effects of vehicle attributes, contextual and social network attributes on the latent demand for electric cars. The study contributes to the existing literature primarily by explicitly modelling the effects of different elements of social networks on the latent demand for electric cars. Moreover, the number of attributes included in the study design exceeds the typical number of attributes used in previous research, making the model more sensitive to a larger spectrum of variables. Two different mixed logit models are estimated: one with random parameters for vehicle attributes and contextual attributes and fixed effects for the social network attributes; one with random effects for social network attributes and fixed effects for the remaining attributes. Results indicate substantive differences between these two models in terms of the shape of utility curves. Overall, vehicle attributes are most important in the choice of electric cars, followed by social influence attributes. The effects of social network are relatively small.     

考虑网络流量的最优路径求解模型和算法

本文旨在解决交通网络中群体车辆的路径选择问题．即为每个车辆寻求最优行驶路径．使之在起迄点间的旅行时间最短．考虑到网络流量对路段旅行时间的影响，先进行流量分配，再同时为各个车辆寻求最短路径．为此，首先给出了考虑流量影响的网络模型，然后建立了基于路段的用于流量分配的变分不等式模型．该模型的解给出了车辆按照最优路径行驶时分配到各路段上的车辆数目．由于该模型是完全基于路段的，从而克服了基于路径方法必须进行路径穷举的缺陷．最后给出了最优路径选择算法，并证明了算法的正确性．本文给出的模型和算法适用于交通畅通、交通拥挤等各种情况．实验结果表明本文提出的模型和算法是非常有效的．     

基于多任务卷积神经网络的车辆多属性识别

细粒度车辆识别极具挑战性,尤其在两辆车的外型差异及其细微的时候。通过车辆的附加属性能够提高车辆识别效果,但一般的神经网络模型忽略了附加属性间的联系,提出一种基于改进的triplet loss作为损失函数的车辆多属性学习的卷积神经网络,用于实现细粒度车辆多属性识别。具体而言,通过对传统神经网络结构的改变,将车辆识别问题转化为多属性学习问题。对三元组损失函数进行改进用于训练网络以实现细粒度车辆识别。同时,创建了一个车辆多属性数据集并完成训练工作,结果显示了该方法的潜力。     

基于AFP的有向加权注意力流网络链路预测

个性化推荐系统在减轻信息超载、提供个性化服务和辅助用户决策等方面应用广泛,链路预测是个性化推荐的重要方法之一。传统启发式链路预测方法仅考虑网络的图结构特征,缺乏对显式特征和隐式特征信息的应用,且大多数方法基于无向无权网络。针对传统链路预测方法存在的不足,基于集体注意力流网络和R-GCN方法,提出了链路预测算法AFP,将注意力流网络中2节点间不同的边方向抽象为2种边关系类型,并引入注意力机制学习网络中的节点属性和边属性,还综合考虑了网络的图结构特征、显式特征和隐式特征,最后通过评分函数得到三元组成立与否的概率,将链路预测问题转化为一个二分类问题,预测节点间的边属于某个关系类型的可能性。实验结果表明,相比于GCN、GAT等6个基准算法,该算法在准确度、精度和召回率等多个评价指标上均有提升。     

Decremental algorithm for adaptive routing incorporating traveler information

Routing in a stochastic and dynamic (time-dependent) network is a crucial transportation problem. A new variant of adaptive routing, which assumes perfect online information of continuous real-time link travel time, is proposed. Driver's speed profile is taken into consideration to realistically estimate travel times, which also involves the stochasticity of links in a dynamic network. An adaptive approach is suggested to tackle the continuous dynamic shortest path problem. A decremental algorithm is consequently developed to reduce optimization time. The impact of the proposed adaptive routing and the performance of the decremental approach are evaluated in static and dynamic networks under different traffic conditions. The proposed approach can be incorporated into vehicle navigation systems.     

Cold-Start Link Prediction via Weighted Symmetric Nonnegative Matrix Factorization with Graph Regularization

Link prediction has attracted wide attention among interdisciplinary researchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks. Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connected graph. However, the complexity of the real world makes the complex networks abstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start link prediction is favored as one of the most valuable subproblems of traditional link prediction. However, due to the loss of many links in the observation network, the topological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topological information from observed network becomes the key point to solve the problem of cold-start link prediction. In this paper, we propose a framework for solving the cold-start link prediction problem, a joint-weighted symmetric nonnegative matrix factorization model fusing graph regularization information, based on low-rank approximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designed graph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain each other. Finally, a unified framework for implementing cold-start link prediction is constructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validation on five real networks with attributes shows that the proposed model has very good predictive performance when predicting missing edges of isolated nodes.     

Optimal Static Load Balancing in Star Network Configurations with Two-Way Traffic

We study the static load balancing problem in a distributed computer system that consists of a set of heterogeneous computer systems interconnected by a star network with two-way traffic. We formulate the static load balancing problem as a nonlinear optimization problem which minimizes the mean response time. We prove that in the optimal solution the satellite nodes in the star network can be divided into four different types: the idle source nodes, the active source nodes, the neutral nodes, and the sink nodes. The necessary and sufficient conditions for optimal solution are studied. An efficient algorithm of complexity O(n) is proposed for the static load balancing of an n-satellite system. The effects of link communication time on optimal load balancing in a star network are also studied by parametric analysis. By employing the proposed algorithm, a significant system performance improvement over that without load balancing is illustrated in a numerical example. The numerical example also shows that the effects of the link communication time in a star network are large.     

Network‐based H∞ control for stochastic systems

This paper investigates the problem of network‐based control for stochastic plants. A new model of stochastic time‐delay systems is presented where both network‐induced delays and packet dropouts are taken into consideration for a sampled‐data network‐based control system. This model consists of two successive delay components in the state, and we solve the network‐based H_∞ control problem based on this model by a new stochastic delay system approach. The controller design for the sampled‐data systems is carried out in terms of linear matrix inequalities. Finally, we illustrate the methodology by applying these results to an air vehicle control problem. Copyright © 2008 John Wiley & Sons, Ltd.     

车联网D2D通信中最大化频谱资源利用率分配算法

无线通信技术快速发展,终端设备不断增多,为缓解这一现象,提升系统网络容量,针对车联网蜂窝D2D(device to device)通信资源分配问题,提出了一种最大化频谱资源利用率分配算法.该算法以最大化频谱资源利用率为优化目标,在满足车联网通信的基本服务质量(quality of service,QoS)下,通过V2V(vehicle to vehi-cle)和V2P(vehicle to people)共享信道资源来提高频谱资源利用率.首先利用信道状态信息定义的链路增益因子为终端用户找到潜在的通信链路集合;然后证明终端用户复用链路资源时功率分配问题为一个凸优化问题,利用凸优化理论求得最优传输功率;随后求解最优的信道匹配问题,此问题为多对一的加权匹配问题,为降低算法复杂度用KM(Kuhn Munkres)算法来求解.仿真结果表明,所提算法较其他算法能够有效地提升系统吞吐量、提高频谱资源利用率、提升网络性能,优化车联网通信资源分配问题.     

基于城市道路网的最短路径分析解决方案

近年来GIS对网络分析功能的需求迅速增长．网络分析中的一个关键问题是最短路径问题，它作为许多领域中速择最优问题的基础，在交通网络分析系统中占有重要地位．由于最短路径分析常用于汽车导航系统以及各种城市应急系统(如l10报警、l19火警以及120急救系统)，本文针对城市道路网的特点，提出了一种实用、高效的最短路径分析解决方案．     

Pareto-Optimal Sustainable Transportation Network Design under Spatial Queuing

This paper presents a multi-objective network design problem with environmental considerations for urban networks with queues. A spatial queuing link model is introduced to take account of the spatial effect of queuing. With this more realistic link performance function capturing spatial queuing, the network equilibrium flow patterns can be more accurately identified. Furthermore, to better estimate vehicle emissions, this paper proposes a refined emission estimation model, which distinguishes between travel speeds in free-running state and queue-forming state over a link. A multi-objective bi-level programming is then developed, in which the upper-level problem optimizes the investment decisions, whereas the lower-level problem characterizes the user equilibrium with spatial queuing delays. The metaheuristic of non-dominated sorting genetic algorithm II (NSGA-II) is adopted to solve the multi-objective network design problem. Numerical tests on the Sioux Falls network and the Barcelona network confirm the effectiveness of our proposed model and algorithm in identifying queuing equilibrium flows and Pareto optimal solutions. The refined models and valuable information about trade-offs among objectives are particularly helpful for environmentally sustainable transport network planning.

     

A Road Timetable to aid vehicle routing and scheduling

Both within and between urban conurbations, the time of a journey and the corresponding shortest path in a road network from an origin to a destination may depend on the time of the day, the day of the week and the season of the year. Significant journey time differences occur mainly due to recurring instances and variations in levels of traffic congestion throughout the year. This paper examines the issues involved in constructing a database of road times for a road network that uses time-dependent data on the travel times for individual roads in the network to provide the expected times and distances between locations for journeys starting at different times. The benefits of time-dependent vehicle routing and scheduling systems are demonstrated by using real-world data for the road network in the north west of England.     

Transmission Link Dimensioning and Service Assignment for Differentiated Protection

We consider the problem of deciding on which transmission systems to carry a given number of services or sessions to minimize link cost and to guarantee a required degree of reliability. The sessions come in a variety of types, each with different requirements and the transmission systems can meet some of these requirements at some cost. We define a new optimization model for the Link Dimensioning and Service Assignment (LDSA) problem, an extension of current work on link dimensioning problems where a common assumption is that there is a single transmission system type on each link. We find that under realistic assumptions about the number of session types and transmission classes, it is possible to solve the LDSA problem to optimality in reasonable time using a combinatorial approach based on the maximal configuration concept. We also propose an efficient stopping criterion for large problems that gives near-optimal solutions within a few seconds. This efficient solution procedure can then be used to solve different network management problems where capacity assignment and survivability issues are involved.     

System capacity for a two-commodity multistate flow network with unreliable nodes and capacity weight

The system capacity of a single-commodity flow network is the maximum flow from the source to the destination. This paper discusses the system capacity problem for a two-commodity multistate flow network composed of multistate components (edges and nodes). In particular, each component has both capacity and cost attributes. Both types of commodity, which are transmitted through the same network simultaneously, consume the capacities of edges and nodes differently. That is, the capacity weight varies with types of commodity, edges and nodes. We first define the system capacity as a 2-tuple vector and then propose a performance index, the probability that the upper bound of the system capacity is a given pattern subject to the budget constraint. Such a performance index can be easily computed in terms of upper boundary vectors. An efficient algorithm based on minimal cuts is thus presented to generate all upper boundary vectors. The manager can apply this performance index to measure the quality level of supply-demand systems such as computer, logistics, power transmission, telecommunication and urban traffic systems.     

User-level Real-Time Network System on Microkernel-based Operating Systems

This paper presents the design and implementation of a user-level real-time network system in Real-Time Mach. Traditional network systems for microkernel based operating systems, which tend to focus on high performance and flexibility, are not suitable for real-time communication. Our network system provides a framework for implementing real-time network protocols which require to bound protocol processing time, and it is suitable for implementing on microkernel based operating systems. In this paper, we especially focus on the aspects to avoid the priority inversion problem in order to make network systems more preemptable and predictable. We also describe the feasibility of our network system for building distributed multimedia systems.     

层析成像技术在网络属性估算中的应用研究

网络层析成像技术在网络属性估算中的应用主要分为网络拓扑判定和链路属性估算两个重要应用.主要讨论通过对网络上的主机进行单播的测量来获得网络的逻辑拓扑及链路属性,并提出进行网络属性估算的新方案.首先介绍基于延时的"三明治"测量方案和相关网络拓扑判定算法,随后通过实验对网络拓扑判定的系统聚类树算法进行了分析,在此基础上提出进行网络属性估算的新方案.     

Integer linear programming formulation of the vehicle positioning problem in automated manufacturing systems

This paper addresses the problem of vehicle location (positioning) for automated transport in fully automated manufacturing systems. This study is motivated by the complexity of vehicle control found in modern integrated circuit semiconductor fabrication facilities where the material handling network path is composed of multiple loops interconnected. In order to contribute to decrease the manufacturing lead-time of semiconductor products, we propose an integer linear program that minimizes the maximum time needed to serve a transport request. Computation experiments are based on real-life data. We discuss the practical usefulness of the mathematical model by means of a simulation experiment used to analyze the factory operational behavior.