Design and development of a hybrid ant colony-variable neighbourhood search algorithm for a multi-depot green vehicle routing problem

The traditional distribution planning problem in a supply chain has often been studied mainly with a focus on economic benefits. The growing concern about the effects of anthropogenic pollutions has forced researchers and supply chain practitioners to address the socio-environmental concerns. This research study focuses on incorporating the environmental impact on route design problem. In this work, the aim is to integrate both the objectives, namely economic cost and emission cost reduction for a capacitated multi-depot green vehicle routing problem. The proposed models are a significant contribution to the field of research in green vehicle routing problem at the operational level. The formulated integer linear programming model is solved for a set of small scale instances using LINGO solver. A computationally efficient Ant Colony Optimization (ACO) based meta-heuristic is developed for solving both small scale and large scale problem instances in reasonable amount of time. For solving large scale instances, the performance of the proposed ACO based meta-heuristic is improved by integrating it with a variable neighbourhood search.     

A probabilistic optimization model for allocating freeway sensors

This paper proposes a sensor location model to identify a sensor configuration that minimizes overall freeway performance monitoring errors while considering the consequences of probabilistic sensor failures. To date, existing sensor location models for freeway monitoring inherently assume that either deployed sensors never fail or the consequences of sensor failure are trivial matters. However, history has revealed that neither assumption is realistic, suggesting that ignoring failures in sensor allocation models may actually produce a significantly suboptimal configuration in the real world. Our work addresses this dilemma by developing a probabilistic optimization model that will minimize the error expectation by examining all possible failure scenarios, each with an occurrence probability. To ensure the scenario completeness and uniqueness, a sensor failure scenario is represented by using a binary string with 1 indicating an operational sensor at a given site and 0 for sensor failure or no sensor deployed. When applied to a case study network, it is shown that an optimal configuration that considers sensor failure is significantly different from an optimal configuration that ignores sensor failure, revealing that sensor failures pose non-trivial consequences on performance monitoring accuracy.     

Routing aspects of electric vehicle drivers and their effects on network performance

This study investigates the routing aspects of battery electric vehicle (BEV) drivers and their effects on the overall traffic network performance. BEVs have unique characteristics such as range limitation, long battery recharging time, and recuperation of energy lost during the deceleration phase if equipped with regenerative braking system (RBS). In addition, the energy consumption rate per unit distance traveled is lower at moderate speed than at higher speed. This raises two interesting questions: (i) whether these characteristics of BEVs will lead to different route selection compared to conventional internal combustion engine vehicles (ICEVs), and (ii) whether such route selection implications of BEVs will affect the network performance. With the increasing market penetration of BEVs, these questions are becoming more important. This study formulates a multi-class dynamic user equilibrium (MCDUE) model to determine the equilibrium flows for mixed traffic consisting of BEVs and ICEVs. A simulation-based solution procedure is proposed for the MCDUE model. In the MCDUE model, BEVs select routes to minimize the generalized cost which includes route travel time, energy related costs and range anxiety cost, and ICEVs to minimize route travel time. Results from numerical experiments illustrate that BEV drivers select routes with lower speed to conserve and recuperate battery energy while ICEV drivers select shortest travel time routes. They also illustrate that the differences in route choice behavior of BEV and ICEV drivers can synergistically lead to reduction in total travel time and the network performance towards system optimum under certain conditions.     

Dynamic green bike repositioning problem – A hybrid rolling horizon artificial bee colony algorithm approach

This paper introduces a new dynamic green bike repositioning problem (DGBRP) that simultaneously minimizes the total unmet demand of the bike-sharing system and the fuel and CO₂ emission cost of the repositioning vehicle over an operational period. The problem determines the route and the number of bikes loaded and unloaded at each visited node over a multi-period operational horizon during which the cycling demand at each node varies from time to time. To handle the dynamic nature of the problem, this study adopts a rolling horizon approach to break down the proposed problem into a set of stages, in which a static bike repositioning sub-problem is solved in each stage. An enhanced artificial bee colony (EABC) algorithm and a route truncation heuristic are jointly used to optimize the route design in each stage, and the loading and unloading heuristic is used to tackle the loading and unloading sub-problem along the route in a given stage. Numerical results show that the EABC algorithm outperforms Genetic Algorithm in solving the routing sub-problem. Computation experiments are performed to illustrate the effect of the stage duration on the two objective values, and the results show that longer stage duration leads to higher total unmet demand and total fuel and CO₂ emission cost. Numerical studies are also performed to illustrate the effects of the weight and the loading and unloading times on the two objective values and the tradeoff between the two objectives.     

一种基于钟形函数的变步长LMS算法

在干扰消除背景下,提出了一种基于钟形函数的变步长LMS自适应滤波算法。通过理论分析和计算机仿真验证了该算法在提高收敛速度和减小稳态误差上的优越性,并分析了参数选取对算法性能的影响。     

基于铜排的新型短路保护用大电流传感器的分析与设计

由于含有铁芯,现有保护装置上铜排用大电流传感器传感器笨重,繁杂并且占用较大体积。这对珍贵的船用空间十分不利。考虑到大电流时铜排周边能对传感器形成大干扰的可能性较小的缘故,提出用固定在铜排特殊位置上的单一芯片替代一整圈铁芯的电流测量方案。本文从霍尔传感器的基本原理出发,用有限元软件分析从实际角度考虑了霍尔芯片的摆放位置,并用试验验证了仿真的准确性。在此基础上,分析了干扰源对其产生的各种影响。最后得出结论:可以仅使用放置在特定位置上的霍尔芯片的方法测量特定铜排上的大电流,这样节省了大量体积。     

TRIZ技术进化在汽车转向系统的应用解析

介绍了发明问题解决理论(TRIZ)中的技术进化理论,包括技术进化法则及进化路线,应用"动态性和可控性进化法则"中的"向流体或场进化路线",解析了汽车转向系统的技术进化历程及未来发展趋势。     

应急救援中多目标车辆路径问题研究

针对突发事件下应急物流的特点,综合考虑救援时间最短、救援成本最低的应急物资车辆路径调度方法。相对于传统的车辆路径模型,模型中引入道路通畅率这一重要因素作为约束条件,建立适合于解决实际问题的模型与算法。实例结果表明:相对于传统的应急车辆路径求解方法,该模型与算法可有效地缩短救援时间,减少救援成本。     

RFID技术在构建船舶物联网系统中的关键技术

物联网技术在现代航运中的应用不断深入拓展,已经在船舶视频监控领域、航运物流运输领域及军事指挥中都得到了发展。RFID技术也称射频识别系统,是整个物联网系统的关键部件之一。本文分析特定海洋环境对RFID系统的影响,重点研究RFID技术中防干扰算法,并在此基础上提出一种新的时序控制ALOHA算法,通过估计RFID系统中电子标签的概率分布及数目,自适应的更新下一时序中的电子标签。最后通过仿真验证算法的有效性。     

对隧道施工地质超前预报工作的反思与探讨

研究目的：寻找在隧道施工地质超前预报实践中存在的问题和预报重点,理清现阶段施工地质超前预报工作思路。研究方法：通过对隧道施工地质问题的回顾与地质预报工作现状调查,对主要的几种隧道施工超前地质预报手段及特点进行归纳,从手段和方法上对存在问题进行分析。研究结果：得出了隧道施工超前地质预报的工作重点是灾害性地质的预报,应结合地质理论对工程安全稳定性进行评价后作出,而不仅仅停留在对不良地质体的探测上;提出了地质超前预报纵、横向探测范围的建议值。研究结论：地质预报工作是信息化施工的延伸,预报应考虑施工过程在地质体上的作用,探测技术可尝试对开挖面前方地质体工程特性进行监测与控制等观点;提倡制定地质预报规程,统一作业水平,建立地质预报评价体系,进行多手段、多方法的协同预报思路。