遗传算法在立体仓库货位优化分配中的研究

存储一定数量货物的自动化仓库中,以基于随机存储策略的库区和货位分配以及堆垛机行驶时间为优化控制目标,针对自动化立体仓库的库区和货位的分配策略问题进行了讨论,提出立体仓库的库区优化数学模型。在库区优化基础上,进一步提出货位优化数学模型,将Pareto最优解的概念与遗传算法相结合,提出了一种解决多目标优化问题的Pareto遗传算法解决货位优化问题,给出了仿真实验及分析。结果表明采用遗传算法优化策略可以有效地解决自动化立体仓库的货位优化分配问题。     

病毒协同进化遗传算法在自动化立体仓库货位优化中应用的研究

自动化立体仓库的存取效率直接影响着现代物流的整体效益,而存取效率高低的关键在于货位优化。针对自动化立体仓库实际应用中的货位规划难题,提出了利用病毒协同进化遗传算法来研究自动化立体仓库货位优化问题的方法,并将该算法和传统的遗传算法作比较。以提高货架稳定性和货物出入库效率为优化目标,建立了货位优化的多目标优化问题数学模型。最后利用MATLAB工具进行编程与仿真,实验结果表明,病毒协同进化遗传算法(VEGA)相比传统的遗传算法具有更好的收敛性和搜索效率。由此可见,利用病毒协同进化遗传算法对自动化立体仓库进行货位优化,可以很大程度上改善货物的出入库效率和货架的稳定性,进而提高货架的使用率。     

自动化立体仓库货位分配优化研究

分析了自动化立体仓库货位优化问题,建立了自动化立体仓库货位优化数学模型,采用Pareto算法研究了货位优化方法,通过实例分析,优化后显著提高了货架稳定性,并且提高了货物存取效率。     

基于改进粒子群算法的立体仓库货位分配优化

研究自动化立体仓库固定货架的货位分配问题,货位分配综合考虑了货架的稳定性和出入库效率,建立了货位优化的数学模型,提出了基于Pareto最优解的改进粒子群算法（PSO）来解决此问题的方法。在优化过程中引用了置换的概念来计算粒子的速度,并且在算法中采用小生境技术提高非劣解集的分散性,用存档群体保存了非劣解。仿真实验证明,此优化策略可以有效地解决自动化立体仓库的货位分配问题。     

自动化仓库货位分配优化问题研究

研究了自动化立体仓库固定货架的货位分配问题。分配货位时需要同时考虑货架稳定性和出入库操作的效率,将这一问题描述为一个组合多目标优化问题,采用遗传算法对这一问题进行了求解,对交叉算子进行了改进,得到的解可兼顾两个优化目标。仿真实验表明这一方法可较好地解决货位分配问题。     

响应动态约束条件的多目标货位优化算法研究

针对自动化立库货位决策与优化问题,考虑到优化目标多样、托盘使用状态及可分配货位动态变化等因素,提出了一种响应动态约束条件的多目标货位优化算法。以巷道作业均衡、货架重心稳定及作业路径最短建立多目标优化模型,基于变异系数自适应差分进化算法,使用货位随机数编码,根据实时货位可行域进行个体解码,以响应动态货位约束条件。提出了基于层次分析的Pareto解评价方法,从而获得多批作业货位持续优化的目标权重,为仓储货位决策提供合理方案。多批作业算法实验结果表明:所提算法效果显著优于多目标简单加权算法,能够有效应用于动态货位决策与优化。     

汽车零部件立体仓库货位优化问题研究

以立体仓库库存为研究对象,从物流仓储管理角度,研究了货位分配优化问题。分 析了汽车零部件货位布局优化原则,建立多目标货位分配优化数学模型,对遗传算法进行了算子 设计,运用Matlab 软件实现模型的求解,得出可行的货位优化方案。最后结合实例进行多目标 货位优化数学模型求解及应用,并以三维仿真图形展示了优化效果,验证了所设计的遗传算法的 有效性,对同类问题的解决具有参考意义。     

基于遗传算法的军队立体仓库货位再分配研究

为提高军队自动化立体仓库出货速度和运行稳定性,提出了在堆垛机闲时对货位进行以分类存储L形分区为导向的再分配优化设计。根据用户需求,生成分类存储的L形分类存储目标货位分区信息,以堆垛机总运行时间最短和货架重心最低为目标,研究货品新的目标耦合货位并建立了相应数学模型,利用基于混合偏好的遗传算法对该多目标优化问题进行了求解。结果显示,该方法能较大提高自动化立体仓库某类货品在特定环境下的出库效率并降低货架重心。同时,该研究对一般意义的货位再分配也具有一定价值。     

智能仓储货位规划与AGV路径规划协同优化算法

智能仓储的优化一般分为货架优化和路径优化两部分.货架优化针对货物与货架两者的关系,对货物摆放位置进行优化;而路径优化主要寻找自动引导小车（Automated Guided Vehicle,AGV）的最优路径规划.目前,大多的智能仓储优化仅对这两部分进行独立研究,在实际仓储应用中只能以线性叠加的方式解决问题,导致问题的求解易陷入局部最优中.本文通过对智能仓储环节中各部分的关系进行耦合分析,提出了货位和AGV路径协同优化数学模型,将货架优化和路径规划归为一个整体;此外,提出了智能仓储协同优化框架的求解算法,包括货品相似度求解算法和改进的路径规划算法;并在以上两种算法的基础上,使用改进的遗传算法,实现了货位路径协同优化.实验结果验证了本文提出的智能仓储协同优化算法的有效性和稳定性.通过使用该算法可有效提高仓储的出货效率,降低运输成本.     

遗传算法在货位分配中的应用研究

本文讨论了货位分配算法。首先建立了货位分配算法的数学模型,然后提出了采用遗传算法解决这个多目标组合优化的问题。最终通过应用验证了算法的适用性。     

Reliable Intermodal Freight Network Expansion with Demand Uncertainties and Network Disruptions

This paper develops a robust Mixed-Integer Linear Program (MILP) to assist railroad operators with intermodal network expansion decisions. Specifically, the objective of the model is to identify critical rail links to retrofit, locations to establish new terminals, and existing terminals to expand, where the intermodal freight network is subject to demand and supply uncertainties. Additional considerations by the model include a finite overall budget for investment, limited capacities on network links and at intermodal terminals, and time window constraints for shipments. A hybrid Genetic Algorithm (GA) is developed to solve the proposed MILP. It utilizes a column generation algorithm to solve the freight flow assignment problem and a multi-modal shortest path label-setting algorithm to solve the pricing sub-problems. An exact exhaustive enumeration method is used to validate the GA results. Experimental results indicate that the developed algorithm is capable of producing optimal solutions efficiently for small-sized intermodal freight networks. The impact of uncertainty on network configuration is discussed for a larger-sized case study.     

多目标优化设计在杀螺剂配方研究中的应用

针对某生物杀螺剂制作中多目标约束问题,提出了一种应用Pareto遗传算法来解决问题的优化方法。建立了用于多目标优化的适应度函数,使用排列选择方法将带约束的多目标问题转换为无约束优化问题;并根据计算中的收敛情况引入了适当的移民算子,改善了遗传算法的进化性能,得到了Pareto最优解集,成功地解决了该生物杀螺剂的最优配方问题。     

A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm

Genetic algorithms (GAs), which are directed stochastic hill climbing algorithms, are a commonly used optimization technique and are generally applied to single criterion optimization problems with fairly complex solution landscapes. There has been some attempts to apply GA to multicriteria optimization problems. The GA selection mechanism is typically dependent on a single-valued objective function and so no general methods to solve multicriteria optimization problems have been developed so far. In this paper, a new method of transformation of the multiple criteria problem into a single-criterion problem is presented. The problem of transformation brings about the need for the introduction of thePareto set estimation method to perform the multicriteria optimization using GAs. From a given solution set, which is the population of a certain generation of the GA, the Pareto set is found. The fitness of population members in the next GA generation is calculated by a distance metric with a reference to the Pareto set of the previous generation. As we are unable to combine the objectives in some way, we resort to this distance metric in the positive Pareto space of the previous solutions, as the fitness of the current solutions. This new GA-based multicriteria optimization method is proposed here, and it is capable of handling any generally formulated multicriteria optimization problem. The main idea of the method is described in detail in this paper along with a detailed numerical example. Preliminary computer generated results show that our approach produces better, and far more Pareto solutions, than plain stochastic optimization methods.     

The multi-criteria minimum spanning tree problem based genetic algorithm

Minimum spanning tree (MST) problem is of high importance in network optimization and can be solved efficiently. The multi-criteria MST (mc-MST) is a more realistic representation of the practical problems in the real world, but it is difficult for traditional optimization technique to deal with. In this paper, a non-generational genetic algorithm (GA) for mc-MST is proposed. To keep the population diversity, this paper designs an efficient crossover operator by using dislocation a crossover technique and builds a niche evolution procedure, where a better offspring does not replace the whole or most individuals but replaces the worse ones of the current population. To evaluate the non-generational GA, the solution sets generated by it are compared with solution sets from an improved algorithm for enumerating all Pareto optimal spanning trees. The improved enumeration algorithm is proved to find all Pareto optimal solutions and experimental results show that the non-generational GA is efficient.     

Design Of Robust Pole Assignment Based On Pareto‐Optimal Solutions

In this paper, a new design method for robust pole assignment based on Pareto‐optimal solutions for an uncertain plant is proposed. The proposed design method is defined as a two‐objective optimization problem in which optimization of the settling time and damping ratio is translated into a pole assignment problem. The uncertainties of the plant are represented as a polytope of polynomials, and the design cost is reduced by using the edge theorem. The genetic algorithm is applied to optimize this problem because of its multiple search property. In order to demonstrate the effectiveness of the proposed design method, we applied the proposed design method to a magnetic levitation system.     

求解多目标最小生成树的一种新的遗传算法

在改进的非支配排序遗传算法（NSGA-II）的基础上,提出了一种新的基于生成树边集合编码的繁殖算子求解多目标最小生成树问题的遗传算法。通过快速非支配排序法,降低了算法的计算复杂度,引入保存精英策略,扩大采样空间。实验结果表明：对于多目标最小生成树问题,边集合编码具有较好的遗传性和局部性,而且基于此繁殖算子的遗传算法在求解效率和解的质量方面都优于基于PrimRST的遗传算法。     

自动化仓储系统不规则货位优化问题研究

针对不规则货位的自动化仓储系统的特点,以提高系统效率和空间利用率为优化控制目标,研究了自动化仓储系统不规则货位优化分配策略,提出了首先对不规则的货位进行货位区优化,对每个货位区进行货位优化的数学模型,提出两级遗传算法解决货位优化问题。结果表明,该优化方法有效地提高了系统的效率,实现了密集存储,为自动化仓储系统中不规则货位的货位分配优化提供了理论依据和实践途径。     

A new approach for online multiobjective optimization of mechatronic systems

We present a new concept for online multiobjective optimization and its application to the optimization of the operating point assignment for a doubly-fed linear motor. This problem leads to a time-dependent multiobjective optimization problem. In contrast to classical optimization where the aim is to find the (global) minimum of a single function, we want to simultaneously minimize k objective functions. The solution to this problem is given by the set of optimal compromises, the so-called Pareto set. In the case of the linear motor, there are two conflicting aims which both have to be maximized: the degree of efficiency and the inverter utilization factor. The objective functions depend on velocity, force and power, which can be modeled as time-dependent parameters. For a fixed point of time, the entire corresponding Pareto set can be computed by means of a recently developed set-oriented numerical method. An online computation of the time-dependent Pareto sets is not possible, because the computation itself is too complex. Therefore, we combine the computation of the Pareto set with numerical path following techniques. Under certain smoothness assumptions the set of Pareto points can be characterized as the set of zeros of a certain function. Here, path following allows to track the evolution of a given solution point through time.