Aggregate-level demand management in evacuation planning

Without successful large-scale regional evacuations, threats such as hurricanes and wild-fires can cause a large loss of life. In this context, automobiles are oftentimes an essential transportation mode for evacuations, but the ensuing traffic typically overwhelms the roadway capacity and causes congestion on a massive scale. Congestion leads to many problems including longer, costlier, and more stressful evacuations, lower compliance rates, and increased risk to the population. Supply-based strategies have traditionally been used in evacuation planning, but they have been proven to be insufficient to reduce congestion to acceptable levels. In this paper, we study the demand-based strategies of aggregate-level staging and routing to structure the evacuation demand, both with and without congestion. We provide a novel modeling framework that offers strategic flexibility and utilizes a lexicographic objective function that represents a hierarchy of relevant evacuation-based goals. We also provide insights into the nature and effect of network bottlenecks. We compare our model with and without congestion in relation to tractability, normative optimality, and robustness under demand uncertainty. We also show the effectiveness of using demand-based strategies as opposed to using the status quo that involves a non-staged or simultaneous evacuation process. Effective solution procedures are developed and tested using hypothetical problem instances as well as using a larger study based on a portion of coastal Virginia, USA.     

SSM and technology management: Developing multimethodology through practice

Growing competition and economic recession is driving the need for more rapid redesign of operations enabled by innovative technologies. The acquisition, development and implementation of systems to manage customer complaints and control the quality assurance process is a critical area for engineering and manufacturing companies. Multimethodologies, and especially those that can bridge ‘soft’ and ‘hard’ OR practices, have been seen as a possible means to facilitate rapid problem structuring, the analysis of alternative process design and then the specification through to implementation of systems solutions. Despite the many ‘hard’ and ‘soft’ OR problem structuring and management methods available, there are relatively few detailed empirical research studies of how they can be combined and conducted in practice. This study examines how a multimethodology was developed, and used successfully, in an engineering company to address customer complaints/concerns, both strategically and operationally. The action research study examined and utilised emerging ‘soft’ OR theory to iteratively develop a new framework that encompasses problem structuring through to technology selection and adoption. This was based on combining Soft Systems Methodology (SSM) for problem exploration and structuring, learning theories and methods for problem diagnosis, and technology management for selecting between alternatives and implementing the solution. The results show that, through the use of action research and the development of a contextualised multimethodology, stakeholders within organisations can participate in the design of new systems and more rapidly adopt technology to address the operational problems of customer complaints in more systemic, innovative and informed ways.     

A multicut L-shaped based algorithm to solve a stochastic programming model for the mobile facility routing and scheduling problem

This paper considers the mobile facility routing and scheduling problem with stochastic demand (MFRSPSD). The MFRSPSD simultaneously determines the route and schedule of a fleet of mobile facilities which serve customers with uncertain demand to minimize the total cost generated during the planning horizon. The problem is formulated as a two-stage stochastic programming model, in which the first stage decision deals with the temporal and spatial movement of MFs and the second stage handles how MFs serve customer demands. An algorithm based on the multicut version of the L-shaped method is proposed in which several lower bound inequalities are developed and incorporated into the master program. The computational results show that the algorithm yields a tighter lower bound and converges faster to the optimal solution. The result of a sensitivity analysis further indicates that in dealing with stochastic demand the two-stage stochastic programming approach has a distinctive advantage over the model considering only the average demand in terms of cost reduction.     

求解柔性作业车间调度的动态群智能优化算法

针对柔性作业车间调度问题,提出一种新型两阶段动态混合群智能优化算法.算法初始阶段采用动态邻域的协同粒子群进行粗搜索,第二阶段提出了基于混沌算子的蜂群进行细搜索,既增强了种群多样性,又提高了算法搜索精度,实现了全局搜索与局部搜索能力的有效平衡.针对柔性作业车间调度问题特点,采用独特的编码方式和位置更新策略来避免不合法解的产生.最后将此算法在不同规模的实例上进行了仿真测试,并与最近提出的其他几种具有代表性的算法进行了比较,验证了算法的有效性和优越性.     

Incorporating inventory and routing costs in strategic location models

We consider a supply chain design problem where the decision maker needs to decide the number and locations of the distribution centers (DCs). Customers face random demand, and each DC maintains a certain amount of safety stock in order to achieve a certain service level for the customers it serves. The objective is to minimize the total cost that includes location costs and inventory costs at the DCs, and distribution costs in the supply chain. We show that this problem can be formulated as a nonlinear integer programming model, for which we propose a Lagrangian relaxation based solution algorithm. By exploring the structure of the problem, we find a low-order polynomial algorithm for the nonlinear integer programming problem that must be solved in solving the Lagrangian relaxation sub-problems. We present computational results for several instances of the problem with sizes ranging from 40 to 320 customers. Our results show the benefits of having an integrated supply chain design framework that includes location, inventory, and routing decisions in the same optimization model.     

Static and dynamic routing and wavelength assignment algorithms for future transport networks

In this paper, two static and three dynamic routing algorithms have been proposed and compared to some of the existing algorithms on the basis of blocking probability. The two proposed static routing and wavelength assignment (RWA) algorithms reduce the blocking probability to maximize the utilization of network. For dynamic algorithms, first a model with no weights assignments is presented and then three algorithms are proposed and analysed with weight assignment resulting in reduction of blocking probability. All these algorithms are analysed and compared with four wavelength assignment schemes which are first-fit, random, most used and least used. It is shown that our proposed static algorithms give the best performance for first-fit wavelength assignment and most used wavelength assignment strategies with reduced complexity. For least used wavelength assignment and random wavelength assignment, 1 fixed and 2 alternate routing algorithm gives the lowest blocking probability. For dynamic routing, it has been shown that our proposed algorithm “less weight to maximum empty and nearest” gives the least blocking probability as compared to the other dynamic routing algorithms for random, most used and least used wavelength assignment strategies.     

基于经验模态分解和支持向量机的车型声频识别

车型自动识别是智能交通系统的重要组成部分。针对现有车型识别存在的问题,提出利用经验模态分解和支持向量机的车型声频识别方法。将车辆行驶的声音信号进行分解,以分解不同模态的能量作为特征向量,并以此作为训练样本对支持向量机构成的车型识别器进行训练,通过对小汽车和卡车的声音信号处理结果表明:利用车辆声音信号能够正确识别不同的车型,识别准确率达95%,是车型识别的有效方法。     

智能车转向系统的研究及设计

智能车的底层控制主要包括油门、刹车和转向三部分。转向部分主要以电子式助力转向系统(EPS)为基础进行设计。电子控制式电动助力转向系统具有较好的操纵性、安全性、轻便性以及节能性等优点。然而,传统汽车上应用的助力转向系统仅起到辅助作用,对于无人驾驶的智能车来说功率过小,无法实现智能车在静止或者低速行驶时的转向控制,并且智能车的转向控制策略也有所不同。通过选择合理的转向电机、合适的传感器,设计了一套适合智能车的转向系统。通过反复试验,该转向系统可以很好的实现智能车转向控制。     

车载终端车联网中间件技术研究与实现

为了解决因车身网络的数据协议差异造成的车载智能终端不能在多个车型上通用的问题,提出了一种车载智能终端中间件架构。在该架构中,采用多层结构隔离应用与网络直接访问。采用组件结构实现了应用的抽象与复用,降低应用开发难度。采用消息总线,负责管理组件之间的数据通信,为应用层提供一个统一接口的虚拟车身网络环境;通过消息的数据通信实现车载终端应用间的数据交互,底层采用通信服务层屏蔽了各种车身网络的差异性,解决了车载终端在不同网络协议车型上使用的通用性问题。最后,通过搭建实际的测试平台验证了该设计的可行性和有效性。