带时间窗车辆路径问题的并行遗传算法

采用并行遗传算法研究了带时间窗限制的车辆路径问题.通过设计并行算法和交叉、变异等算子提高了算法的计算效率和性能.通过计算若干benchmark问题,验证了模型的有效性.     

一种改进的SWEEP路径策略

针对需求随机的随机车辆路径问题,提出了一种改进的SWEEP路径策略.建立了基于该策略的车辆任务量分配多目标规划模型,给出了求解该模型的启发式算法.通过24个不同规模的VRP问题的仿真实验,证明了提出的任务分配模型和算法具有较强的适用性;改进的SWEEP策略能充分利用运输资源,减少运输成本.     

基于复合形算子的基础支护桩优化设计智能算法研究

本文通过遗传算法和传统复合形搜索法相结合,基于对遗传算法算子计算结构的调整,并将遗传算法与神经网络相结合,提出并研究了一种新的优化设计方法,协同求解复杂工程中的优化问题.并针对悬臂式支护桩的优化设计的数学模型,采用该算法进行了优化设计分析;计算结果表明,该算法可克服遗传算法最终进化至最优解较慢和人工神经网络易陷入局部解的缺陷,具有较好的全局性和收敛速度.     

不确定车数的随机车辆路径问题模型及算法

研究了一类有时间约束、车辆数量不确定的随机车辆路径问题;建立了该类问题的随机规划数学模型;设计了模型求解的遗传算法、禁忌搜索算法和遗传-禁忌混合算法.禁忌算法采用了对当前解的车辆-顾客分配结构和解的路径顺序分别禁忌的双层禁忌算法,使算法全局性更好,同时也降低了搜索时间.把禁忌算法作为变异算子应用于遗传算法形成了混合算法.最后给出了计算示例,对算法进行了比较分析.     

基于分段遗传编码的配送路径优化

为减小物流配送中车辆路径优化这一NP难题的组合规模，增大其搜索空间及寻优效率，提出了一种分段编码方法，编码中的各段表示相应车辆路径的需求城市集合。以非完全连通配送网络为研究对象，基于分段遗传编码，构造了车辆路径优化问题的遗传算法，通过对编码各段超载、包容等的判断，得出一个关于需求城市的最优划分。最后利用改进的迪杰斯特拉算法，根据最优化分中的需求城市，求取最优的配送路径集。计算示例证明了该方法的有效性。     

考虑废物包装时间的车辆回收路径规划方法

针对考虑废物包装时间的车辆回收路径规划问题,建立问题数学模型,提出禁忌搜索算法与模因算法求解该问题,并与爬山算法、遗传算法进行对比.模因算法是爬山算法和遗传算法的结合.实验结果表明:在解的质量方面,禁忌搜索算法与模因算法所求出的解的质量要远远好于另外两种算法,但在运行时间上,禁忌搜索、爬山算法与遗传算法要远优于模因算法.     

混合智能算法在CVRPTW中的应用

考虑到遗传算法本身存在易"早熟收敛"的缺陷,提出将模拟退火算法中的Metropolis接受准则引入到遗传算法的群体更新策略中,并将其应用于物流管理中的带容量约束和时间窗的车辆路径问题(CVRPTW).针对Solomon提出的几个标准问题,从数值计算上探索了遗传算法和模拟退火算法融合后的优化能力,获得了满意的效果.     

基于混合编码遗传算法的匹配追踪算法

匹配追踪算法可以将任一信号分解为一组冗余的基元函数的线形展开,但是计算信号在每一个基元上的投影需要很高的代价,从而影响了它的广泛应用.本文提出了一种用浮点数和格雷码的混合编码遗传算法来实现匹配追踪算法,该算法有机结合了遗传算法和匹配追踪算法的优点,不仅能够有效地降低计算量,而且能够提高计算精度.计算机仿真结果证实了该方法的准确性和应用价值.     

基于密度峰值聚类的VRPTW问题研究

提出一种密度峰值聚类 (density peak clustering, DPC)与遗传算法(genetic algorithm, GA)相结合的新型混合算法(density peak clustering with genetic algorithm, DGA),求解带时间窗的车辆路径问题。首先应用DPC对客户进行聚类以缩减问题规模,再将聚类后的客户用GA进行线路优化。结果表明：DGA在9个数据集上的平均值比模拟退火(simulated annealing, SA)和禁忌搜索(Tabu)分别提高了13.41%和4.7%,单个数据集最大提高了26.4%。这证明了该算法是求解车辆调度问题的高效算法。     

基于混沌遗传算法的圆度误差测量

提出了一种基于混沌遗传算法的计算圆度误差的新方法.它利用混沌优化方法的遍历性和随机性,通过混沌扰动操作可克服传统遗传算法中的早熟问题,确保算法的全局收敛性.该方法满足最小条件原理,其计算结果的精确度非常高,理论上可以获得全局最优解.实例计算表明,这种算法简单明确,具有精度高、收敛速度快、易于计算机程序实现和推广应用等特点.     

带时间窗的汽车总装线物料配送路径规划

分析了现阶段汽车总装线物料配送中存在的问题,建立了适用于汽车总装线物料配送路径规划的混合时间窗模型,提出了解决带时间窗的汽车总装线物料配送路径优化问题的改进遗传算法,使用了一种新的染色体编码方式和与之对应的交叉算子。针对传统轮盘赌随机操作选择误差比较大的弊端,提出改进的轮盘赌选择算子,加大随机数的产生次数并加入排序选择的思想,融合了最佳个体保存选择策略,提高算子的选优性能。实验表明该算法用于求解带时间窗的汽车总装线物料配送路径问题的有效性。     

Research on Vehicle Routing Problem with Soft Time Windows Based on Hybrid Tabu Search and Scatter Search Algorithm

With the expansion of the application scope of social computing problems, many path problems in real life have evolved from pure path optimization problems to social computing problems that take into account various social attributes, cultures, and the emotional needs of customers. The actual soft time window vehicle routing problem, speeding up the response of customer needs, improving distribution efficiency, and reducing operating costs is the focus of current social computing problems. Therefore, designing fast and effective algorithms to solve this problem has certain theoretical and practical significance. In this paper, considering the time delay problem of customer demand, the compensation problem is given, and the mathematical model of vehicle path problem with soft time window is given. This paper proposes a hybrid tabu search (TS) & scatter search (SS) algorithm for vehicle routing problem with soft time windows (VRPSTW), which mainly embeds the TS dynamic tabu mechanism into the SS algorithm framework. TS uses the scattering of SS to avoid the dependence on the quality of the initial solution, and SS uses the climbing ability of TS improves the ability of optimizing, so that the quality of search for the optimal solution can be significantly improved. The hybrid algorithm is still based on the basic framework of SS. In particular, TS is mainly used for solution improvement and combination to generate new solutions. In the solution process, both the quality and the dispersion of the solution are considered. A simulation experiments verify the influence of the number of vehicles and maximum value of tabu length on solution, parameters’ control over the degree of convergence, and the influence of the number of diverse solutions on algorithm performance. Based on the determined parameters, simulation experiment is carried out in this paper to further prove the algorithm feasibility and effectiveness. The results of this paper provide further ideas for solving vehicle routing problems with time windows and improving the efficiency of vehicle routing problems and have strong applicability.     

包装物回收物流中的车辆路径优化问题

目的提高遗传算法(GA)求解包装物回收车辆路径优化问题的性能。方法通过对传统GA算法的改进,提出混合蜂群遗传算法(HBGA)。首先改进传统GA算法的初始种群生成方式,设计初始种群混合生成算子;其次,提出最大保留交叉算子,对优秀子路径进行保护;然后,在上述改进的基础上引入蜜蜂进化机制,用以保证种群多样性和优秀个体特征信息的利用程度;最后,对标准算例集进行仿真测试。结果与传统GA算法相比,HBGA算法在全局寻优能力、算法稳定性和运行速度方面均有所改善。HBGA算法的全局寻优能力和算法稳定性均优于粒子群算法(PSO)、蚁群算法(ACO)和禁忌搜索算法(TS),但运行速度稍慢于TS算法。结论对传统GA算法的改进是合理的,且HBGA算法整体求解性能优于PSO算法、ACO算法和TS算法。     

A cloud theory-based particle swarm optimization for multiple decision maker vehicle routing problems with fuzzy random time windows

This article puts forward a cloud theory-based particle swarm optimization (CTPSO) algorithm for solving a variant of the vehicle routing problem, namely a multiple decision maker vehicle routing problem with fuzzy random time windows (MDVRPFRTW). A new mathematical model is developed for the proposed problem in which fuzzy random theory is used to describe the time windows and bi-level programming is applied to describe the relationship between the multiple decision makers. To solve the problem, a cloud theory-based particle swarm optimization (CTPSO) is proposed. More specifically, this approach makes improvements in initialization, inertia weight and particle updates to overcome the shortcomings of the basic particle swarm optimization (PSO). Parameter tests and results analysis are presented to highlight the performance of the optimization method, and comparison of the algorithm with the basic PSO and the genetic algorithm demonstrates its efficiency.     

考虑油耗的车辆调度问题模型与算法

针对目前运输行业中能源消耗已经成为影响社会环境和运行成本的重要因素这一实际,在分析汽车行驶油耗规律和定量描述的基础上,引入考虑能源消耗的车辆调度问题,考虑了车辆运行能耗目标以及客户的需求时间窗等约束,建立问题的精确数学模型。由于问题具有NP hard性质,为了求解实际大规模此类问题而设计了一种新型禁忌搜索算法。该算法引入大规模邻域搜索思想,并提出基于虚拟车辆的新型搜索邻域。通过算例试验验证了算法的有效性。     

带软时间窗的同时取送货车辆路径问题研究

考虑软时间窗下的车辆路径问题,客户点常伴有同时取送货的双重需求。针对此类问题,通过对软时间窗、车辆在途前后时间关系及二者融合问题进行刻画,同时将车辆行驶距离、车辆使用数、违反软时间窗总时间、客户满意度等纳入综合考量,构建相应混合整数非线性规划(mixed integer nonlinear programming, MINLP)模型。设计相应多目标优化求解算法,运用理想点法对目标函数进行转化,将多目标优化问题转化为单目标优化问题。结合相应算例集,运用LINGO 17.0全局求解程序求得每组算例的全局最优解。结果表明,针对带软时间窗的同时取送货车辆路径问题(vehicle routing problem with simultaneous pick-up and delivery and soft time windows, VRPSPDSTW),所建模型及算法是有效且可行的。     

A MCVRP-based model for PCB assembly optimisation on the beam-type placement machine

The beam-type placement machine is capable of picking up multiple components simultaneously from the feeders in printed circuit board (PCB) assembly. Simultaneous pickup occurs only if the heads in the beam are aligned with the feeders and the nozzle-types on these heads match with the component-types on the feeders. In order to minimise the assembly cycle time, the optimisation problem is decomposed into two sub-problems, the pickup combination and sequencing problem, and the placement cluster and sequencing problem. These two sub-problems are simultaneously solved by the proposed hybrid genetic algorithm (HGA). The pickup combination and sequencing problem is similar to the popular multi-compartment vehicle routing problem (MCVRP); a genetic algorithm (GA) for the MCVRP is therefore modified and applied to solving the pickup combination and sequencing problem. A greedy heuristic algorithm is used to solve the placement cluster and sequencing problem. The numerical experiments reveal that the HGA outperforms the algorithms proposed by previous papers.