考虑客户满意度的网购物流配送路径优化

为更全面地反映网购物流配送的客户满意度,引入客户满意度指标.首先,考虑城市交通管制的硬时间窗约束和客户要求的软时间窗约束,建立最小配送成本和最短配送时间的多目标优化模型;然后,引入客户满意度指标,并设定客户满意度阈值,对该模型进行优化.设计遗传算法求解该优化模型.结合实际算例对模型和算法的有效性进行验证.结果表明:优化后的模型可以在保证较低配送成本和较少配送时间的基础上,最大化配送服务的客户满意度水平.该方法不存在客户流失的风险,有利于提高企业的竞争优势,符合企业的长远利益.

软硬时间窗共存装卸一体化车辆路径问题的混合离散粒子群优化算法

文章针对软硬时间窗共存装卸一体化车辆路径问题(vehicle routing problem with simultaneous delivery and pickup under coexistence of soft and hard time windows,VRPSDPCSHTW)建立了包含车辆固定出行成本、运输成本和惩罚成本的数学模型,提出了一种混合离散粒子群优化算法。针对基本离散粒子群算法容易早熟收敛而陷入局部最优等问题,内嵌一种变邻域下降局域搜索方法,并在一定概率下执行以加强种群搜索能力,最后通过3个算例的仿真分析进行了算法验证。

带硬时间窗的航空路径规划模型

针对物流快递行业,以牺牲部分成本来完全满足快递时效的要求,建立了基于硬时间窗的航空路径规划模型,采用并行遗传算法进行了求解.同时建立了相应的软时间窗模型,通过构造满意度函数,与硬时间窗的结果进行了对比分析,算例结果表明硬时间窗的规划路线虽然成本较高,但是更注重时效性的客户的满意度也高,这种规划方法更适于中端物流企业的特点与要求.

带外包的服饰运输调度问题的优化

由于服饰产品是一种时效性很强的商品,而且服饰产品在配送过程中可以外包给快递公司进行配送,对带外包和硬时间窗的服饰运输调度问题(Apparel products Vehicle Routing Problem with Hard Time Windows and Outsourcing,AVRPHTWO)进行分析,并构建了AVRPHTWO、一般性VRP(Vehicle Routing Problem)和VRPSTW(Vehicle Routing Problem with Soft Time Windows)的数学模型,通过对基本的人工鱼群算法(artificial fish swarm algorithm,AFSA)进行改进,混沌搜索被引入人工鱼群算法来提高算法的全局收敛性,反馈策略用来指导人工鱼的移动,以此来提高收敛精度。应用混沌人工鱼群算法(chaotic artificial fish swarm algorithm,CAFSA)及遗传算法(genetic algorithm,GA)对所建立的三种模型求解,通过对实验数据进行处理,证明了AVRPHTWO模型和混沌人工鱼群算法求解此类模型的有效性,进一步证明了问题模型的复杂程度影响算法寻优能力,问题模型简单时,遗传算法更优;问题模型复杂时,混沌人工鱼群算法更优。

基于改进蚁群算法的软硬时间窗车辆路径优化

文章针对客户对时间紧迫性要求不同的情形,建立软硬时间窗车辆路径优化模型,在车辆行驶距离和载重约束下,以行驶成本、惩罚成本和固定成本形成的总成本最低为目标,利用改进蚁群算法优化车辆路径。首先蚂蚁状态转移规则采用随机规则使蚂蚁优先选择时间窗较窄和到达时间较早的节点,接着采用伪随机规则决定蚂蚁倾向选择信息素浓度较大的路径或随机选择,并且探讨伪随机因子q0取值对解的影响并找到最优值,同时对不满足硬时间窗约束的节点做返回到配送中心的处理。最后通过实例验证,Matlab仿真计算,采用伪随机规则且使用最优的q0值,使配送成本降低且总优化率提高了17%,进一步论证改进蚁群算法有优于遗传算法的收敛效果。

An evolutionary algorithm approach for the constrained multi-depot vehicle routing problem

Purpose-The purpose of this paper is to explore a real world vehicle routing problem(VRP)that has multi-depot subcontractors with a heterogeneous fleet of vehicles that are available to pickup/deliver jobs with varying time windows and locations.Both the overall job completion time and number of drivers utilized are analyzed for the automated job allocations and manual job assignments from transportation field experts.Design/methodology/approach-A nested genetic algorithm(GA)is used to automate the job allocation process and minimize the overall time to deliver all jobs,while utilizing the fewest number of drivers-as a secondary objective.Findings-Three different real world data sets were used to compare the results of the GA vs transportation field experts’manual assignments.The job assignments from the GA improved the overall job completion time in 100 percent(30/30)of the cases and maintained the same or fewer drivers as BS Logistics(BSL)in 47 percent(14/30)of the cases.Originality/value-This paperprovidesa novel approach to solving a real world VRPthathasmultiple variants.While there have been numerous models to capture a select number of these variants,the value of this nested GA lies in its ability to incorporate multiple depots,a heterogeneous fleet of vehicles as well as varying pickup times,pickup locations,delivery times and delivery locations for each job into a single model.Existing research does not provide models to collectively address all of these variants.