生鲜类物流配送网络选址-路径优化问题研究

为解决生鲜类物流配送网络选址-路径优化问题,构建了基于服务质量最优化、物流节点建造成本及物流运营服务成本最小化的多目标两层级物流配送网络选址-路径优化问题数学模型,并通过改进遗传算法求解最优方案.对遗传算法中的算子进行优化,解决了传统遗传算法求解过程中无法求得全局最优解以及易陷入局部最优解的现象.通过选取通州区部分区域为背景进行模型验证,得出优化后成本节约了15.71%,说明该模型具有良好的参考价值.     

基于动态遗传算法的共享配送模式研究

针对传统配送中配送车辆装载率低、车辆数量多及配送成本高等问题,提出不同类型零售商资源共享的城市配送优化方法,并考虑配送中的车辆油耗与不确定需求等问题。以总配送成本最低为目标建立模型,利用基于动态参数的改进遗传算法对模型进行求解。最后,通过算例对共享配送模式与算法进行测试。结果表明,共享配送模式能够有效减低车辆数量、提高装载率及降低配送成本,同时改进遗传算法能够高效、准确对模型求解。     

考虑时间和油耗的拖轮多目标优化调度研究

进出港口的大型船舶需向港口申请拖轮协助以进行靠离泊作业。拖轮调度是港口重要的计划事项之一。针对拖轮调度过程中需要平衡完工时间和油耗量以提高港口服务水平和降低拖轮公司经营成本的问题，本文以最小化拖轮最大完工时间和最小化拖轮总油耗量为目标，构建了混合整数规划拖轮多目标优化调度模型。模型还考虑了潮汐港口大量船舶在潮水期间集中进出港的特点，并根据拖轮在调度过程中的不同状态分类计量其产生的油耗量，以使模型更接近实际状况。为求解模型，运用了带有精英策略的非支配排序遗传算法(NSGA-II),算法采用一维实数编码，以事件建模思想设置适应度函数，并结合拖轮调度特点设计了遗传算子，求得的Pareto前沿解和算法对比验证了该算法的有效性。最后，以广州港港口拖轮调度实际运作数据作为算例，验证了模型的可行性与有效性，为港口拖轮调度计划提供了决策依据。     

基于存档策略的多目标优化的遗传算法及其收敛性分析

设计了一种用遗传算法求解多目标优化问题的有效方法——基于存档策略的多目标优化的遗传算法,并讨论了此算法的收敛性.首先给出档案的定义,设计出基于支配关系下的带有存档策略遗传算法,并通过算例检验了算法的有效性;然后引入了两档案间的距离的概念,在此距离定义的基础上证明了算法在概率意义下是收敛的.     

支持向量机中一种参数优化选取方法

本文给出一种支持向量机中的参数优化选取方法. 它是通过遗传算法和确定性算法相结合解平衡约束优化问题,求出二分类支持向量机(SVM)中的正则参数C,本文将C作为优化问题中的变量来处理.遗传算法用来求解以C为变量的优化问题, 而确定性算法对每一个C值求解约束.数值计算的结果表明,用文中所述的方法求得的C值能明显提高支持向量机的泛化性能.     

油田注水系统拓扑布局优化的混合遗传算法

以投资最小为目标函数,建立了注水系统拓扑布局优化数学模型.根据模型特点,将优化问题分为两层,分别采用遗传算法和非线性优化方法进行求解.并对遗传算法的操作过程进行了改进,调整了适应函数,改进了交叉和变异操作,结合了模拟退火算法,在操作过程中使约束条件得到满足,减少了不可行解的产生,使遗传算法的优化性能得到了提高.优化算例说明了该方法的有效性.     

主通道位置不固定的设施布局问题多目标优化模型及算法

以物流中心设施布局问题为对象,提出了考虑出入口及主通道位置不固定情况下的设施布局问题的多目标优化模型并设计了其改进的遗传算法。首先,以物料搬运成本最小、活动关系密切度最大和面积利用率最大为目标,构建了考虑出入口位置不固定条件下的具有I型主通道的设施布局多目标优化数学模型。然后,设计了一种改进的遗传算法,包括:改进的编码、解码方法,追加了解码修正操作,基于惩罚函数策略的适应度函数等。实例测试表明,本算法的执行效率高而且结果稳定,优化效果好,布局结果紧凑适用。     

基于灰关联分析的GA-BP神经网络在汽车油耗估算中的应用

从设计参数特征入手分析影响汽车油耗的因素,利用灰关联分析方法,解析了各设计参数对汽车油耗的影响程度,选择其中灰关联度较大的设计参数作为输入数据,综合工况油耗作为输出数据,构建6-5-1层结构的BP神经网络预测模型,并利用遗传算法获得优化后的BP神经网络的权值和阈值,然后训练BP神经网络得到最优值,最后以国内市场340款汽车作为研究样本,进行有效性验证.研究结果表明,模型利用灰关联分析获得影响汽车油耗的主要因素,简化了网络结构;与优化前的BP神经网络相比,具有更高的预测精度和可靠性.     

优化交通信号配时 缓解城市交通拥堵

为了缓解城市交通拥堵,建立以延误时间最短、停车次数最少为目标函数的非线性优化模型,用遗传算法进行计算求解.计算结果表明,所得的优化信号配时,降低了平均延误时间,减少了平均停车次数,提高了交叉口通行能力.     

基于遗传算法的固定起讫点危险品配送路线优化

为科学选择危险品配送路线,保障运输安全,将传统TSP(Travelling SalesmanProblem)问题加以推广和延伸,建立以路段交通事故率、路侧人口密度、环境影响因子和路段运输费用为指标的固定起讫点危险品配送路线优化模型.以遗传算法基本框架为基础,引入新的遗传算子,构建了可用于实现模型的多目标遗传算法.实例仿真表明,所建模型和算法在求解固定起讫点危险品配送路线优化问题中有较好的实用性.     

Solving the Dial-a-Ride problem using genetic algorithms

In the Dial-a-Ride problem (DARP), customers request transportation from an operator. A request consists of a specified pickup location and destination location along with a desired departure or arrival time and capacity demand. The aim of DARP is to minimize transportation cost while satisfying customer service level constraints (Quality of Service). In this paper, we present a genetic algorithm (GA) for solving the DARP. The algorithm is based on the classical cluster-first, route-second approach, where it alternates between assigning customers to vehicles using a GA and solving independent routing problems for the vehicles using a routing heuristic. The algorithm is implemented in Java and tested on publicly available data sets. The new solution method has achieved solutions comparable with the current state-of-the-art methods.     

Reliability based assignment in stochastic-flow freight network

Based on the reliability of transportation time, a transportation assignment model of stochastic-flow freight network is designed in this paper. This transportation assignment model is built by mean of stochastic chance-constraint programming and solved with a hybrid intelligent algorithm (HIA) which integrates genetic algorithm (GA), stochastic simulation (SS) and neural network (NN). GA is employed to report the optimal solution as well as the optimal objective function values of the proposed model. SS is used to simulate the value of uncertain system reliability function. The uncertain function approximated via NN is embedded into GA to check the feasibility and to compute the fitness of the chromosomes. These conclusions have been drawn after a test of numerical case using the proposed formulations. System reliability, total system cost and flow on each path would finally reach at their own convergence points. Increase of the system reliability causes increase of the total time cost. The system reliability and the total time cost converge at a possible Nash Equilibrium point.     

考虑外协服务的车辆路径优化问题

降低零售企业的末端配送成本是控制物流成本的关键,共享经济的发展为此提供了新思路。因此,针对零售企业末端上门配送服务成本较高的情况,提出了考虑外协的车辆服务策略,将有意愿进行单次交付的线下客户作为协作车辆配合普通车辆来完成线上客户订单的配送,建立了以最小化普通车辆路径成本,普通车辆使用成本,时间窗惩罚成本和协作车辆补偿成本为目标函数的数学模型,并设计匹配算法和混合遗传算子的模拟退火算法对该模型进行求解,最后结合算例对提出的算法进行检验与分析。     

多时间窗车辆路径问题的智能水滴算法

研究了多时间窗车辆路径问题,考虑了车容量、多个硬时间窗限制等约束条件,以动用车辆的固定成本和车辆运行成本之和最小为目标,建立了整数线性规划模型。根据智能水滴算法的基本原理,设计了求解多时间窗车辆路径问题的快速算法,利用具体实例进行了模拟计算,并与遗传算法的计算结果进行了对比分析,结果显示,利用智能水滴算法求解多时间窗车辆路径问题,能够以很高的概率得到全局最优解,是求解多时间窗车辆路径问题的有效算法。     

部分联合运输策略下的物流车辆路径优化问题研究

论文分析了物流车辆路径优化问题的特点,提出了企业自营物流和第三方物流协同运输的部分联合运输策略。根据客户需求节点的特点进行了节点分类,建立了以车辆调用成本、车辆运输成本、第三方物流运输成本之和最小为目标的整数线性规划模型。根据部分联合运输策略下各类客户需求点运输方式特点,构造了一种新的变维数矩阵编码结构,并对传统算法中概率选择操作方式进行修改,提出了一种新的智能优化算法并与枚举法和遗传算法的运算结果进行了算法性能对比分析。结果显示,本文提出的逆选择操作蚁群算法具有较快的运算速度和较高的稳定性,是求解此类问题的一种有效算法。     

Contrast enhancement for image by WNN and GA combining PSNR with information entropy

A new contrast enhancement algorithm for image is proposed combining genetic algorithm (GA) with wavelet neural network (WNN). In-complete Beta transform (IBT) is used to obtain non-linear gray transform curve so as to enhance global contrast for an image. GA determines optimal gray transform parameters. In order to avoid the expensive time for traditional contrast enhancement algorithms, which search optimal gray transform parameters in the whole parameters space, based on gray distribution of an image, a classification criterion is proposed. Contrast type for original image is determined by the new criterion. Parameters space is, respectively, determined according to different contrast types, which greatly shrink parameters space. Thus searching direction of GA is guided by the new parameter space. Considering the drawback of traditional histogram equalization that it reduces the information and enlarges noise and background blur in the processed image, a synthetic objective function is used as fitness function of GA combining peak signal-noise-ratio (PSNR) and information entropy. In order to calculate IBT in the whole image, WNN is used to approximate the IBT. In order to enhance the local contrast for image, discrete stationary wavelet transform (DSWT) is used to enhance detail in an image. Having implemented DSWT to an image, detail is enhanced by a non-linear operator in three high frequency sub-bands. The coefficients in the low frequency sub-bands are set as zero. Final enhanced image is obtained by adding the global enhanced image with the local enhanced image. Experimental results show that the new algorithm is able to well enhance the global and local contrast for image while keeping the noise and background blur from being greatly enlarged.     

带时间窗的三维装载物流配送优化方法研究

针对城市物流配送优化研究在客户服务时间窗和货物装载方式合理结合方面存在的不足,考虑物流配送车厢货物装载方式与客户访问序列相关的特征对车厢空间进行合理的区域划分。首先,构建了包含配送中心的固定成本、配送车辆的运输成本、维修成本、租赁成本和违反时间窗惩罚成本的物流运营成本最小化和配送车辆空间利用率最大化的双目标优化模型;然后,提出一种结合遗传算法(GA)全局搜索能力和禁忌搜索算法(TS)局部搜索能力的GA-TS混合算法求解模型;最后,结合重庆市某配送中心的三维装载物流配送实例数据进行了优化计算,实验结果给出了带时间窗的三维装载物流配送路径优化方案,并进行了不同车厢空间分区模式下平均装载率、物流运营成本和车辆使用数的比较分析。研究表明,当客户需求货物种类数与车辆的空间区域划分数相等且按货物类型进行区域划分时,物流运营成本最小,配送车辆使用数最少和车辆平均装载率最高。     

Optimised crossover genetic algorithm for capacitated vehicle routing problem

This paper presents a genetic algorithm for solving capacitated vehicle routing problem, which is mainly characterised by using vehicles of the same capacity based at a central depot that will be optimally routed to supply customers with known demands. The proposed algorithm uses an optimised crossover operator designed by a complete undirected bipartite graph to find an optimal set of delivery routes satisfying the requirements and giving minimal total cost. We tested our algorithm with benchmark instances and compared it with some other heuristics in the literature. Computational results showed that the proposed algorithm is competitive in terms of the quality of the solutions found.     

An efficient multi-objective PSO algorithm assisted by Kriging metamodel for expensive black-box problems

The huge computational overhead is the main challenge in the application of community based optimization methods, such as multi-objective particle swarm optimization and multi-objective genetic algorithm, to deal with the multi-objective optimization involving costly simulations. This paper proposes a Kriging metamodel assisted multi-objective particle swarm optimization method to solve this kind of expensively black-box multi-objective optimization problems. On the basis of crowding distance based multi-objective particle swarm optimization algorithm, the new proposed method constructs Kriging metamodel for each expensive objective function adaptively, and then the non-dominated solutions of the metamodels are utilized to guide the update of particle population. To reduce the computational cost, the generalized expected improvements of each particle predicted by metamodels are presented to determine which particles need to perform actual function evaluations. The suggested method is tested on 12 benchmark functions and compared with the original crowding distance based multi-objective particle swarm optimization algorithm and non-dominated sorting genetic algorithm-II algorithm. The test results show that the application of Kriging metamodel improves the search ability and reduces the number of evaluations. Additionally, the new proposed method is applied to the optimal design of a cycloid gear pump and achieves desirable results.     

An efficient genetic algorithm for the traveling salesman problem with precedence constraints

The traveling salesman problem with precedence constraints (TSPPC) is one of the most difficult combinatorial optimization problems. In this paper, an efficient genetic algorithm (GA) to solve the TSPPC is presented. The key concept of the proposed GA is a topological sort (TS), which is defined as an ordering of vertices in a directed graph. Also, a new crossover operation is developed for the proposed GA. The results of numerical experiments show that the proposed GA produces an optimal solution and shows superior performance compared to the traditional algorithms.