子母穿梭车式立体仓库复合作业路径优化

目的研究子母穿梭车式立体仓库中复合作业路径优化问题,有利于提高系统运行效率,降低成本。方法对于子母穿梭车式立体仓库在一次存取货作业中复合作业方式的实际调度路径,考虑到其运动机构的加(减)速度,以完成复合作业总时间最短为目标建立数学模型。针对该系统复合作业的运行特征,提出一种结合遗传和蚁群算法各自优点的混合粒子群算法进行优化求解。结果实例验证可知,与粒子群算法和蚁群算法相比,文中提出的混合粒子群算法具有性能稳定、优化效率更高等优点。结论文中所提复合作业路径优化方法能够有效地缩短子母穿梭车式立体仓库的复合作业时间,提高了进出库调度效率。     

考虑半托盘出库的堆垛机复合作业拣选路径优化研究

结合自动化立体仓库中堆垛机执行出入库作业的实际情况，提出了考虑半托盘出库情况下的堆垛机复合作业拣选路径优化问题，并构建了该问题的数学模型。针对研究问题的特点，利用一种离散烟花算法进行模型求解，采用爆炸算子和变异算子执行烟花的爆炸操作，实现算法的全局搜索和局部搜索；应用精英选择策略和类似轮盘赌策略以提高收敛效果。最后以某烟草物流公司自动化立体仓库的堆垛机拣选作业为例，将离散烟花算法与其他算法的运算结果作对比，验证了该算法更具求解优势。     

一种评价通信网节点重要性的新方法——节点孤立法

提出了一种评价通信网节点重要性的新方法--节点孤立法,并提出了节点核度积的概念,认为通信网中最重要的节点是孤立后所对应的节点核度积最大的节点.该方法考虑了网络的连接状况,并且动态地考虑了网络中所有节点相互通信的最短路径总长度的增加值.该方法是基于全网性能对节点重要性进行评价,通过比较节点的核度积,判断通信网中任意两个节点的相对重要性,而且使用了一种模2的矩阵运算方法,可以快速得到节点通信的最短路径.理论分析和仿真实验结果表明,该方法的计算复杂度和对节点重要性评价的准确性优于其它算法.     

基于遗传算法的家用保安机器人路径规划方法

将领域知识与遗传算法相结合,提出了一种针对家用保安机器人的路径规划方法.该算法采用改进的栅格化方法来描述家庭环境,重新定义了路径适应度函数的评价方法,并设计有效的路径遗传算子.仿真结果表明了该算法的正确性和有效性.利用该算法实现了在实际家庭环境下保安机器人的路径规划与动态避障.     

基于遗传算法的码垛机器人路径规划应用

目的为了改进传统遗传算法在码垛机器人路径规划中可能出现的局部陷阱和过早收敛问题,以及机器人的能耗和路线平滑性问题,提出一种改进的遗传算法机器人路径规划方法。方法针对传统遗传算法存在的问题,分别对种群初始化、适应度函数、选择算子、交叉算子、变异算子的算法和方式进行调整和改进,对优秀算法进行融合。针对基本遗传算法主要着重于路径最短,从而忽视了机器人的能耗及路径平滑性等问题,设计一种综合考虑距离和转弯次数控制的适应度函数,最后将改进的算法应用于码垛机器人的路径规划中。结果仿真结果表明,相较于基本遗传算法,提出的算法搜索到的路径质量更高,不仅距离更短,同时转弯次数远远小于其他算法,路径更为平滑,验证了该算法的有效性。结论基于该算法的码垛机器人路径在兼顾距离最优的同时,路线更加平滑。由于减少了转向次数,机器人的能耗更低,同时仿真结果表明,该算法的实时性也较好。     

用于移动机器人路径规划的曲线轨迹算法

针对移动机器人路径规划方法--人工势场法在相近障碍物之间不能发现路径的局限性,经改进,提出了一种圆弧路径算法.该改进算法从轨迹方程的角度出发,将移动机器人的路径以规则曲线的形式进行规划,确定路径方程,并以圆的曲线方程来表示,以求便于计算,同时综合考虑了路径规划的实时性和最短路径,从而可得出最优路径.构建了三维激光扫描系...     

双载具式多层穿梭车立体仓库货位优化研究

目的为提高双载具式多层穿梭车立体仓库的运作效率,对其货位优化进行研究。方法针对双载具式多层穿梭车立体仓库货位优化作业系统的特点,考虑其运动过程中的加(减)速度特性,建立以入库时间最短为目标的货位分配优化模型。通过设计结合交叉变异算子的混合植物繁殖算法对该模型进行优化求解,从而确定最佳货位分配方案,缩短货位分配时间。结果实例分析表明,混合植物繁殖算法与遗传算法和物繁殖算法相比,收敛速度更快、优化效率更高。结论混合植物繁殖算法能够有效地缩短双载具式多层穿梭车立体仓库的货位分配时间,提高了仓库运作效率。     

面向冷链智能立体仓库的数字孪生系统构建

目的 当前冷链物流行业立体仓库存在货品损耗率高、失温严重、信息化水平低等问题,确保冷链智能立体仓库的食品质量安全,并实现降本增效。方法 面向冷链智能立体仓库的运行场景,设计全映射与多维仿真的数字孪生五维模型,通过对多源异构数据的融合,实现立体仓库全要素的状态实时可视化与监测预警,并提出一种支持系统联动决策的模型优化策略,对立体库负载与温控区热力分布进行智能调节,最大限度提高运行效率和降低运营成本。结果 构建了一套冷链智能立体仓库数字孪生系统,通过对优化前后仓库运行数据进行对比,同等仓储环境条件下生鲜蔬菜的食品保鲜度损耗率同比下降了25%~30%,实现了冷链智能立体仓库实时数据与动态虚拟模型的精准映射与联动运转。结论 以广东某供应链管理有限公司佛山智能冷链仓储物流中心的冷链立体仓库为实践对象,形成了冷链智能立体仓库一体化管控新模式。为冷链智能立体仓库运营管理、调度联动决策与协同作业提供了数字孪生技术的应用参考,为建立更完善的保鲜产品包装与冷链食品质量安全提供了新路径。     

Dijkstra算法在公交换乘最短路径中的应用

Dijkstra算法是求加权图中从某一源点到其余各点最短路径的算法,通过对传统的Dijkstra算法进行分析,提出一种改进算法,使其适合应用于求公交换乘最短路径问题。     

基于退火算法的物流配送网的求优研究

模拟退火算法是一种启发式算法,是受到加热紧缩的退火过程所启发而提出来一种求解组合优化问题的一种逼近算法。算法要优于传统的贪婪算法,避免了陷入局部最优的可能,从而达到全局最优解。在物流配送网络中经常为有一些寻求最短路径等问题出现,为了能够达到最短、最优、最经济等,需要进行物流配送路径寻优。文中采用模拟退火算法进行一个示例的验证,效果证明可行。     

基于Flexsim对某医药物流中心立体仓库优化

目的 利用Flexsim软件仿真医药物流中心立体仓库的运作,缩短立体仓库出入库作业时间,优化运作过程人员与设备的组合。方法 以某大型医药物流中心为研究对象,根据实际立体仓库的布局,使用Flexsim仿真软件进行建模仿真时,首先采用遗传算法确定最优的出入库顺序,将结果在Flexsim中仿真运行,证明算法用于出入库仿真的实用性;然后对立体仓库整体流程进行仿真。结果 优化后的流程,出入库作业效率提高了12.4%,确立了仓储作业在(2,2,2,2)配置下,实现了人员设备的最优利用。结论 采用建模仿真的方法,找出了流程中的瓶颈,确认了最优出入库作业顺序,以及人员、设备组合,提高了医药物流中心的出入库效率,降低了设备、人员等成本。     

A Heuristic Programming Algorithm for Warehouse Location

A new heuristic programming method of solving a particular type of warehouse location problem is presented. The problem is to allocate K or less facilities to N possible locations so as to service M demand centers at minimum cost. The algorithm presented is suitable for hand calculation of medium-size problems (50 × 50) or when computerized will readily solve large-scale problems of the order of (600 × 600); i.e., 600 demand centers and 600 possible locations.     

基于鱼骨型仓库布局的多车拣选路径问题优化

为了推动鱼骨型仓库在实际场景下的应用,针对鱼骨型仓库布局下的拣货路径优化问题,构建待拣货点距离计算模型和以有载重、容积限制的多车拣货距离最短为总目标的拣选路径优化模型。考虑遗传算法(GA)全局搜索能力强、粒子群算法(GAPSO)收敛速度快以及蚁群算法(ACO)较强的局部寻优能力,提出一种解决拣选路径优化模型的混合算法(GA-PSO-ACO)。通过不同订单规模的仿真实验,得出该混合算法在适应度值、迭代次数、收敛速度等方面均优于GA算法和GAPSO算法,且在订单规模较大时,平均适应度值约降低8%,有效缩短了总拣选距离,验证了混合算法在解决鱼骨型仓库布局下的拣货路径问题的先进性和有效性,为解决此类仓库内部的拣货路径问题提供新的解决方法和思路。     

基于误差信号的双模式遗传盲均衡算法研究

DD算法具有收敛速度快,稳态剩余误差小的优点,是一种最常用的盲均衡算法。遗传盲均衡算法是一种新的盲均衡算法,它利用遗传算法来解决盲均衡问题,具有较好的全局优化特性。在综合研究了DD算法及遗传盲均衡算法的特性后,利用误差信号的特性,给出了一种新的双模式遗传盲均衡算法,有效的结合了DD算法以及遗传盲均衡算法的优点,大大提高了算法的运算速度,提升了收敛性能,解决了遗传盲均衡算法运算量大的问题,并通过计算机仿真验证了该算法的有效性。     

不确定情景下应急物资储备库选址问题研究

针对在地震等自然灾害发生时受灾点以及应急需求均为不确定的情况,研究了灾前预置应急物资储备库的选址问题。通过设计多个需求情景来描述受灾点与应急需求的不确定性,建立了有最大运输距离限制的鲁棒优化模型,并设计了鲁棒优化方法。通过数值计算比较分析鲁棒优化方法和随机优化方法的计算结果,表明鲁棒优化解受不确定因素产生的偏差要比随机优化解小,鲁棒优化方法能够有效地减弱不确定性因素对选址方案的影响,并且能降低由预测偏差带来的风险。     

Genetic-algorithms-based algorithm portfolio for inventory routing problem with stochastic demand

This paper presents an algorithm portfolio methodology based on evolutionary algorithms to solve complex dynamic optimisation problems. These problems are known to have computationally complex objective functions, which make their solutions computationally hard to find, when problem instances of large dimensions are considered. This is due to the inability of the algorithms to provide an optimal or near-optimal solution within an allocated time interval. Therefore, this paper employs a bundle of evolutionary algorithms (EAs) tied together with several processors, known as an algorithm portfolio, to solve a complex optimisation problem such as the inventory routing problem (IRP) with stochastic demands. EAs considered for algorithm portfolios are the genetic algorithm and its four variants such as the memetic algorithm, genetic algorithm with chromosome differentiation, age-genetic algorithm, and gender-specific genetic algorithm. In order to illustrate the applicability of the proposed methodology, a generic method for algorithm portfolios design, evaluation, and analysis is discussed in detail. Experiments were performed on varying dimensions of IRP instances to validate different properties of algorithm portfolio. A case study was conducted to illustrate that the set of EAs allocated to a certain number of processors performed better than their individual counterparts.     

改进的单亲遗传算法求解倒垛问题

针对仓储物流中常常遇到的倒垛问题进行了分析,建立了0-1非线性整数规划模型,根据模型特点将其归结为具有问题特征的可扩展广义指派问题,该问题具有NP-难的性质,因此获得较大规模最优解是不可能的或是非常困难的.为了求解此问题,本文设计了一种特殊序号编码方式的单亲遗传算法,同时为了保持群体多样性,嵌入了启发式规则进行扰动.仿真算例表明改进后的算法较传统的遗传算法更具有效性.     

An ant colony system-based hybrid algorithm for square root concave cost transhipment problems

Concave cost transhipment problems are difficult to optimally solve for large-scale problems within a limited period of time. Recently, some modern meta-heuristics have been employed for the development of advanced local search based or population-based stochastic search algorithms that can improve the conventional heuristics. Besides these meta-heuristics, the ant colony system algorithm is a population-based stochastic search algorithm which has been used to obtain good results in many applications. This study employs the ant colony system algorithm, coupled with some genetic algorithm and threshold accepting algorithm techniques, to develop a population based stochastic search algorithm for efficiently solving square root concave cost transhipment problems. The developed algorithms are evaluated with a number of problem instances. The results indicate that the proposed algorithm is more effective for solving square root concave cost transhipment problems than other recently designed local search based algorithms and genetic algorithm.     

Use of genetic algorithms to solve production and operations management problems: A review

Operations managers and scholars in their search for fast and good solutions to real-world problems have applied genetic algorithms to many problems. While genetic algorithms are promising tools for problem solving, future research will benefit from a review of the problems that have been solved and the designs of the genetic algorithms used to solve them. This paper provides a review of the use of genetic algorithms to solve operations problems. Reviewed papers are classified according to the problems they solve. The basic design of each genetic algorithm is described, the shortcomings of the current research are discussed and directions for future research are suggested.     

A heuristic approach for balancing mixed-model assembly line of type I using genetic algorithm

The mixed model assembly line is becoming more important than the traditional single model due to the increased demand for higher productivity. In this paper, a set of procedures for mixed-model assembly line balancing problems (MALBP) is proposed to make it efficiently balance. The proposed procedure based on the meta heuristics genetic algorithm can perform improved and efficient allocation of tasks to workstations for a pre-specified production rate and address some particular features, which are very common in a real world mixed model assembly lines (e.g. use of parallel workstations, zoning constraints, resource limitation). The main focus of this study is to study and modify the existing genetic algorithm framework. Here a heuristic is proposed to reassign the tasks after crossover that violates the constraints. The new method minimises the total number of workstation with higher efficiency and is suitable for both small and large scale problems. The method is then applied to solve a case of a plastic bag manufacturing company where the minimum number of workstations is found performing more efficiently.