以均衡为目标的车辆调度问题研究

针对物流配送企业私人承包车辆以及均衡配送线路工作量等新的物流管理理念,提出新的车辆调度模型,将车辆调度问题（VRP）转换为车辆选型和车辆路径优化两个子问题,设计Ak-FirstFit混合算法解决车辆选型问题,运用遗传算法解决车辆路径问题。基于JAVA编写计算机程序,运行结果表明该两阶段算法能够在有效时间内得到满意解。与基于传统物流管理理念的模型进行比较,得出新的两阶段模型能充分体现兼顾效率与公平思想的结论,其理念在物流配送企业中具有相当的借鉴性和推广前景。     

带时间窗的应急救助物资配送车辆路径优化模型研究

在自然灾害突发后,必须将救助物资在规定的时间内运送到受灾点。针对带有时间窗的应急救助物资配送车辆路径优化问题进行研究,建立车辆路径优化模型并运用了蚁群算法进行求解,取得了较好的效果。     

A Green Vehicle Routing Problem

A Green Vehicle Routing Problem (G-VRP) is formulated and solution techniques are developed to aid organizations with alternative fuel-powered vehicle fleets in overcoming difficulties that exist as a result of limited vehicle driving range in conjunction with limited refueling infrastructure. The G-VRP is formulated as a mixed integer linear program. Two construction heuristics, the Modified Clarke and Wright Savings heuristic and the Density-Based Clustering Algorithm, and a customized improvement technique, are developed. Results of numerical experiments show that the heuristics perform well. Moreover, problem feasibility depends on customer and station location configurations. Implications of technology adoption on operations are discussed.     

低功耗分簇路由算法LEACH的能耗分析

文章对无线传感器网络低功耗分簇路由协议的代表性算法—LEACH的运行机制以及性能做了详细的研究,针对该算法的分簇阶段、簇的建立阶段以及稳定的数据传输阶段的相关原理和运行情况作了深入分析。最后从正反两方面总结了LEACH协议的运行特性。     

时变路网无能力约束车辆路径优化

讨论了一类时变路网下的无能力约束车辆配送路径优化问题,建立了基于时变路网的以配送总耗时最短为优化目标的无能力约束车辆配送路径优化模型。提出了基于实时Dijkstra算法的模型求解方法。数值算例表明考虑路网时变特性得到的优化配送方案将更加符合配送实际。     

机场地面保障服务车辆协同调度和仿真优化

为实现大型机场地面保障服务高效化、智能化运行管理,通过研究多航班、多种地服车辆、多车场、带容量时间窗的复杂车辆路径规划问题,构建地服资源协同调度模型。结果表明：根据航班到达规律和机位分布情况,基于分解的高维进化算法求解效果较好,得到车辆使用数少、行驶距离短、超出时间窗惩罚函数最小和任务均衡的四维多目标优化问题的调度优化方案;通过Anylogic仿真软件验证,可实现机位占用情况、车辆使用情况动态分配和监控。     

海上无线网状网中基于Q-Learning的自适应路由算法

针对海上无线网状网通信环境复杂多变、船舶节点具有特殊移动模型等特点，提出一种基于Q-Learning的自适应路由（Q-Learning Based Adaptive Routing，QLAR）算法。综合考虑海上无线电波传播特性、船舶航程信息以及相应海区气象信息等因素的影响，提出链路可靠性、链路稳定性和节点航程相似度等概念，并对链路状态进行评估；然后，根据链路状态评估结果，利用Q-Learning算法寻找源、目的节点间最稳定的路径以传输数据分组；最后，利用OPNET搭建仿真平台对算法进行测试。仿真结果表明，与4种对比算法中性能最优的算法相比，QLAR算法最高可提升分组投递率4.89%，降低平均分组时延17.42%，减少归一化路由开销21.99%。     

LLN中基于能量均衡的高效低时延路由算法

针对有损低功耗网络(Low-power and Lossy Network,LLN)中由于无线链路的有损特性以及节点部署不均匀无法有效实现网络能量均衡，提出一种基于能量均衡的高效低时延路由算法(Efficient Low Latency Routing Algorithm Based on Energy Balance in Low-Power and Lossy Networks，ELLEB-RPL)。该算法采用“ELLEB组网”策略，使得节点能够有效避免在DODAG(Destination Oriented Directed Acyclic Graph)间来回切换；通过采用“最优父节点选择”策略，使得节点选择的父节点达到最优；通过采用“流量自适应分配”策略，达到网络能量均衡的目的。仿真结果表明，ELLEB-RPL在Sink节点平均吞吐量、网络平均寿命以及端到端时延上均有提升。     

The benefit of advance load information for truckload carriers

This paper models and measures the profit improvement trucking companies can achieve by collaborating with their clients to obtain advance load information (ALI). The main approach is to formulate a comprehensive and flexible mixed integer mathematical model and implement it in a dynamic rolling horizon context. The findings illustrate that access to the second and the third day ALI can improve the profit by averages of 22% and 6%, respectively. We also found that the impact of ALI depends on radius of service and trip length but is statistically independent of load density and fleet size.     

An integer programming approach to the bloodmobile routing problem

Every day, a blood center must determine a set of locations among a group of potential sites to route their vehicles for blood collection so as to avoid shortfalls. In this study, a vehicle routing problem is modeled using an integer programming approach to simultaneously identify number of bloodmobiles to operate and minimize the distance travelled. Additionally, the model is extended to incorporate uncertainty in blood potentials and variable durations in bloodmobile visits. Optimal routings are determined using CPLEX solver and branch-and-price algorithm. Results show that proposed algorithm solve the problem to optimality up to 30 locations within 3600 s.