A set-covering model for a bidirectional multi-shift full truckload vehicle routing problem

This paper introduces a bidirectional multi-shift full truckload transportation problem with operation dependent service times. The problem is different from the previous container transport problems and the existing approaches for container transport problems and vehicle routing pickup and delivery are either not suitable or inefficient. In this paper, a set covering model is developed for the problem based on a novel route representation and a container-flow mapping. It was demonstrated that the model can be applied to solve real-life, medium sized instances of the container transport problem at a large international port. A lower bound of the problem is also obtained by relaxing the time window constraints to the nearest shifts and transforming the problem into a service network design problem. Implications and managerial insights of the results by the lower bound results are also provided.     

Ship speed optimization: Concepts,models and combined speed-routing scenarios

The purpose of this paper is to clarify some important issues as regards ship speed optimization at the operational level and develop models that optimize ship speed for a spectrum of routing scenarios in a single ship setting. The paper’s main contribution is the incorporation of those fundamental parameters and other considerations that weigh heavily in a ship owner’s or charterer’s speed decision and in his routing decision, wherever relevant. Various examples are given so as to illustrate the properties of the optimal solution and the various trade-offs that are involved.     

Design and development of a hybrid artificial bee colony algorithm for the environmental vehicle routing problem

The vehicle routing problem (VRP) is a critical and vital problem in logistics for the design of an effective and efficient transportation network, within which the capacitated vehicle routing problem (CVRP) has been widely studied for several decades due to the practical relevance of logistics operation. However, CVRP with the objectives of minimizing the overall traveling distance or the traveling time cannot meet the latest requirements of green logistics, which concern more about the influence on the environment. This paper studies CVRP from an environmental perspective and introduces a new model called environmental vehicle routing problem (EVRP) with the aim of reducing the adverse effect on the environment caused by the routing of vehicles. In this research, the environmental influence is measured through the amount of the emission carbon dioxide, which is a widely acknowledged criteria and accounts for the major influence on environment. A hybrid artificial bee colony algorithm (ABC) is designed to solve the EVRP model, and the performance of the hybrid algorithm is evaluated through comparing with well-known CVRP instances. The computational results from numerical experiments suggest that the hybrid ABC algorithm outperforms the original ABC algorithm by 5% on average. The transformation from CVRP to EVRP can be recognized through the differentiation of their corresponding optimal solutions, which provides practical insights for operation management in green logistics.     

Short-term work team scheduling models for effective road repair and management

Abstract

In this study, we focus on the development of work team routing/scheduling models incorporating stochastic travel and repair times. Robust and expected optimization concepts, combined with a time–space network technique, are used to develop the models. We perform numerical tests based on operational data for Taoyuan County in Taiwan. The test results show the good performance of the models.     

基于博弈的无线Mesh网络高效可靠路由算法

由于无线网络节点自身资源有限,末端节点往往会表现出自私行为以保证其生存时间和工作效率.针对这些特点提出了一种基于博弈的能量容忍策略（ETS）模型,该模型在经典针锋相对（TFT）策略的基础上,加入合作度及能量容忍因子,宽容节点一定程度的自私行为,并给予自私节点改过自新的机会,使其能够恢复合作.同时,基于ETS设计一种路由激励机制,通过奖惩激励自私节点放弃自私行为,并对能量相对较低的节点进行保护,在一定程度上避免个别繁忙节点过度消耗,均衡网络能量,延长了网络的运行时间.实验表明ETS激励机制在防止节点自私行为、优化网络吞吐率和网络生存时间方面等表现出较好的性能优势.     

时间依赖型绿色车辆路径问题研究

城市配送系统中考虑交通拥堵和环境污染车辆路径问题的时间依赖性体现在：不同道路 拥堵程度下车辆运行速度不同,则不同出发时间对应的运输总时间也不同,导致运输成本和造成 的环境污染也有较大差异。因此,本文提出一个时间依赖型绿色车辆路径模型,通过优化运输路 径和出发时间降低运输成本、减少环境污染。模型的目标函数最小化包括油耗成本在内的运输 总成本,其中,车辆油耗的度量基于综合模式排放模型,其创新之处在于,定义了允许车辆在节点 处等待的情形,使车辆选择合适的时间出发以规避拥堵,即通过优化车辆路径以及路径上各节点 处的出发时间寻求成本最优的运输方案。本文提出嵌套遗传算法求解模型,外层遗传算法优化 路径,内层遗传算法优化路径上各节点处的车辆出发时间。并通过响应面分析法(RSM)调试算法 关键参数,得到适用于模型的最佳参数搭配,算法性能测试结果表明了本文算法的高效性。本文 基于污染-路径问题实验数据库进行数值实验,结果证明,允许车辆在客户处等待并选择合适时 间出发,可以在一定程度上降低燃油消耗和总成本。此外,目标函数中引入油耗要素,可以有效 降低决策方案的燃油消耗,减少环境污染。     

多约束条件下城市道路环卫车优化配置与路径规划

为有效解决当前城市环卫车调度决策过度依赖人工主观经验造成的车辆配置不合理和作业效率低下问题,本文提出一种多约束条件下的城市道路环卫车优化配置与路径规划方法。通过综合考虑环卫车在现实作业中的作业时限、服务次数、行驶速度和车辆退出节点等多种约束条件,将物理道路网络拓展为时空网络,刻画车辆在路网上的时空运行轨迹,进而将环卫车优化配置与路径规划问题从数学上描述为带多类约束条件的弧路径问题(Arc Routing Problem, ARP)。 在此基础上,构建以车辆配置与出行总成本最小化为目标函数的环卫车优化配置与路径规划模型,并通过设计分支定价算法精确求解模型。将所提方法应用于苏州工业园区19个区域路网的环卫车辆配置与调度管理,从经济成本、作业效率和环保效益这3方面评估所提方法的可行性和有效性。结果表明：本文所提方法能够显著节约环卫运营成本,提升环卫车辆作业效率;同时,可有效降低环卫车辆碳排放量,形成良好的经济和社会效益,为智慧环卫提供了行之有效的新方案。     

基于遗传算法的道路车辆自组织网络源路由机制

在当前基于交叉路口的城市车辆自组织网络(VANETs)路由协议中，道路上数据包传输大多采用基于地理位置的贪婪转发策略，当数据量较大时，个别节点负载较重，极易引起传输延迟增大乃至丢包的情况.本文提出了一种基于遗传算法的源路由机制，通过记录单体车辆的驾驶信息而非传统方法中的车流均值数据，来预测道路上车辆网络的连通情况，并借助遗传算法，首次基于道路连通性、节点负载和连接跳数这3 点综合考虑，计算得出道路上最佳的源路由节点序列.仿真实验结果表明，在传输率与延迟时间上，性能均优于传统的贪婪路由机制，尤其在车流量为250 veh· lane-1· h-1时，传输率提升约13%.该研究可为智能交通信息通讯提供可靠助力.     

基于蚂蚁算法的动态分布式路由算法

提出了一种利用蚂蚁算法进行动态更新分布式路由表的方法,该方法以路由表中的信息素强度和当时链路的负载状况为基础,来选择路由和更新路由表,给出了更新路由表的规则并利用计算机进行了仿真验证.仿真结果表明,蚂蚁算法可兼顾路由长度和网络负载两方面的因素,其选路效果优于传统动态分布式算法.