Selecting between Sequential Zoning and Simultaneous Zoning for Picker-to-parts Order Picking System Based on Order Cluster and Genetic Algorithm

The existing research of sequential zoning system and simultaneous zoning system mainly focuses on some optimization problems such as workload balance,product assignment and simulation for each system separately.But there is little research on comparative study between sequential zoning and simultaneous zoning.In order to help the designers to choose the suitable zoning policy for picker-to-parts system reasonably and quickly,a systemic selection method is presented.Essentially,both zoning and batching are order clustering,so the customer order sheet can be divided into many unit grids.After the time formulation in one-dimensional unit was defined,the time models for each zoning policy in two-dimensional space were established using filling curves and sequence models to link the one-dimensional unit grids.In consideration of ＂U＂ shaped dual tour into consideration,the subtraction value of order picking time between sequential zoning and simultaneous zoning was defined as the objective function to select the suitable zoning policy based on time models.As it is convergent enough,genetic algorithm is adopted to find the optimal value of order picking time.In the experimental study,5 different kinds of order/stock keeping unit（SKU） matrices with different densities d and quantities q following uniform distribution were created in order to test the suitability of sequential zoning and simultaneous zoning to different kinds of orders.After parameters setting,experimental orders inputting and iterative computations,the optimal order picking time for each zoning policy was gotten.By observing whether the delta time between them is greater than 0 or not,the suitability of zoning policies for picker-to-parts system were obtained.The significant effect of batch size b,zone number z and density d on suitability was also found by experimental study.The proposed research provides a new method for selection between sequential zoning and simultaneous zoning for picker-to-parts system,and improves the rationality and efficiency of selection process in practical design.

Improved quantum bacterial foraging algorithm for tuning parameters of fractional-order PID controller

The quantum bacterial foraging optimization(QBFO)algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is discrete and constant,which cannot affect the situation of the solution space and limit the diversity of bacterial population. In this paper, an improved QBFO(IQBFO) algorithm is proposed, which can adaptively make the quantum rotation angle continuously updated and enhance the global search ability. In the initialization process, the modified probability of the optimal rotation angle is introduced to avoid the existence of invariant solutions. The modified operator of probability amplitude is adopted to further increase the population diversity.The tests based on benchmark functions verify the effectiveness of the proposed algorithm. Moreover, compared with the integerorder PID controller, the fractional-order proportion integration differentiation(PID) controller increases the complexity of the system with better flexibility and robustness. Thus the fractional-order PID controller is applied to the servo system. The tuning results of PID parameters of the fractional-order servo system show that the proposed algorithm has a good performance in tuning the PID parameters of the fractional-order servo system.

Taboo Search Algorithm for Item Assignment in Synchronized Zone Automated Order Picking System

The idle time which is part of the order fulfillment time is decided by the number of items in the zone; therefore the item assignment method affects the picking efficiency. Whereas previous studies only focus on the balance of number of kinds of items between different zones but not the number of items and the idle time in each zone. In this paper, an idle factor is proposed to measure the idle time exactly. The idle factor is proven to obey the same vary trend with the idle time, so the object of this problem can be simplified from minimizing idle time to minimizing idle factor. Based on this, the model of item assignment problem in synchronized zone automated order picking system is built. The model is a form of relaxation of parallel machine scheduling problem which had been proven to be NP-complete. To solve the model, a taboo search algorithm is proposed. The main idea of the algorithm is minimizing the greatest idle factor of zones with the 2-exchange algorithm. Finally, the simulation which applies the data collected from a tobacco distribution center is conducted to evaluate the performance of the algorithm. The result verifies the model and shows the algorithm can do a steady work to reduce idle time and the idle time can be reduced by 45.63% on average. This research proposed an approach to measure the idle time in synchronized zone automated order picking system. The approach can improve the picking efficiency significantly and can be seen as theoretical basis when optimizing the synchronized automated order picking systems.

Design of Second Order Sliding Mode and Sliding Mode Algorithms:A Practical Insight to DC-DC Buck Converter

This paper presents a simple and systematic approach to design second order sliding mode controller for buck converters.The second order sliding mode control(SOSMC)based on twisting algorithm has been implemented to control buck switch mode converter.The idea behind this strategy is to suppress chattering and maintain robustness and finite time convergence properties of the output voltage error to the equilibrium point under the load variations and parametric uncertainties.In addition,the influence of the twisting algorithm on the performance of closed-loop system is investigated and compared with other algorithms of first order sliding mode control such as adaptive sliding mode control(ASMC),nonsingular terminal sliding mode control(NTSMC).In comparative evaluation,the transient response of the output voltage with the step change in the load and the start-up response of the output voltage with the step change in the input voltage of buck converter were compared.Experimental results were obtained from a hardware setup constructed in laboratory.Finally,for all of the surveyed control methods,the theoretical considerations,numerical simulations,and experimental measurements from a laboratory prototype are compared for different operating points.It is shown that the proposed twisting method presents an improvement in steady state error and settling time of output voltage during load changes.

WFRFT modulation recognition based on HOC and optimal order searching algorithm

A hybrid carrier（HC） scheme based on weighted-type fractional Fourier transform（WFRFT） has been proposed recently.While most of the works focus on HC scheme＇s inherent characteristics, little attention is paid to the WFRFT modulation recognition.In this paper, a new theory is provided to recognize the WFRFT modulation based on higher order cumulants（HOC）. First, it is deduced that the optimal WFRFT received order can be obtained through the minimization of 4 th-order cumulants, C_（42）. Then, a combinatorial searching algorithm is designed to minimize C_（42）.Finally, simulation results show that the designed scheme has a high recognition rate and the combinatorial searching algorithm is effective and reliable.

A Second Order Training Algorithm for Multilayer Feedforward Neural Networks

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AN INFEASIBLE-INTERIOR-POINT PREDICTOR-CORRECTOR ALGORITHM FOR THE SECOND-ORDER CONE PROGRAM

A globally convergent infeasible-interior-point predictor-corrector algorithm is presented for the second-order cone programming （SOCP） by using the Alizadeh- Haeberly-Overton （AHO） search direction. This algorithm does not require the feasibility of the initial points and iteration points. Under suitable assumptions, it is shown that the algorithm can find an -approximate solution of an SOCP in at most O（√n ln（ε0/ε）） iterations. The iteration-complexity bound of our algorithm is almost the same as the best known bound of feasible interior point algorithms for the SOCP.

A MODIFIED HIGH-ORDER CUMULANT MUSIC ALGORITHM FOR SIGNAL DOA ESTIMATION

This paper gives a MUSIC signal DOA estimation algorithm based on the modified high-order cumulant matrix which is constructed by the recieved data and their conjugate rearrangements. When the snapshot number is limited, this algorithm can improve the signal DOA estimation performances obviously, and its computational complexity scarcely increases. Finally, some simulation results to verify the theoretical analyses are presented.

A Compound Algorithm of Denoising Using Second-Order and Fourth-Order Partial Differential Equations

In this paper,we propose a compound algorithm for the image restoration. The algorithm is a convex combination of the ROF model and the LET model with a parameter functionθ.The numerical experiments demonstrate that our compound algorithm is efficient and preserves the main advantages of the two models.In particular, the errors of the compound algorithm in L2 norm between the exact images and corresponding restored images are the smallest among the three models.For images with strong noises,the restored images of the compound algorithm are the best in the corresponding restored images.The proposed algorithm combines the fixed point method, an improved AMG method and the Krylov acceleration.It is found that the combination of these methods is efficient and robust in the image restoration.

Stable Model Order Reduction Method for Fractional-Order Systems Based on Unsymmetric Lanczos Algorithm

This study explores a stable model order reduction method for fractional-order systems. Using the unsymmetric Lanczos algorithm, the reduced order system with a certain number of matched moments is generated. To obtain a stable reduced order system, the stable model order reduction procedure is discussed. By the revised operation on the tridiagonal matrix produced by the unsymmetric Lanczos algorithm, we propose a reduced order modeling method for a fractional-order system to achieve a satisfactory fitting effect with the original system by the matched moments in the frequency domain. Besides, the bound function of the order reduction error is offered. Two numerical examples are presented to illustrate the effectiveness of the proposed method.

Two new predictor-corrector algorithms for second-order cone programming

Based on the ideas of infeasible interior-point methods and predictor-corrector algorithms, two interior-point predictor-corrector algorithms for the second-order cone programming （SOCP） are presented. The two algorithms use the Newton direction and the Euler direction as the predictor directions, respectively. The corrector directions belong to the category of the Alizadeh-Haeberly-Overton （AHO） directions. These algorithms are suitable to the cases of feasible and infeasible interior iterative points. A simpler neighborhood of the central path for the SOCP is proposed, which is the pivotal difference from other interior-point predictor-corrector algorithms. Under some assumptions, the algorithms possess the global, linear, and quadratic convergence. The complexity bound O（rln（εo/ε）） is obtained, where r denotes the number of the second-order cones in the SOCP problem. The numerical results show that the proposed algorithms are effective.

Optimization of a global seventh-order dissipative compact finite-difference scheme by a genetic algorithm

A global seventh-order dissipative compact finite-difference scheme is optimized in terms of time stability. The dissipative parameters appearing in the boundary closures are assumed to be different, resulting in an optimization problem with several parameters determined by applying a generic algorithm. The optimized schemes are analyzed carefully from the aspects of the eigenvalue distribution, the ε-pseudospectra, the short time behavior, and the Fourier analysis. Numerical experiments for the Euler equations are used to show the effectiveness of the final recommended scheme.

ADAPTIVE ALGORITHM FOR CONSISTENTMA PARAMETER ESTIMATION VIATHIRD ORDER CUMULANT

This paper addresses the problem of adaptive,consistent parameter estimation for a MA model from the 3rd order cumulant of the system output. The proposed adaptive algorithm is derived by using the new linear equation system (J. K. Tugnait, 1990), which is proved to have unique solution,and hence guarantees the consistence of the MA parameters. Simulation results are provided to show the performance of the new algorithm.

A New Eighth Order Implicit Block Algorithms for the Direct Solution of Second Order Ordinary Differential Equations

This paper focuses on derivation of a uniform order 8 implicit block method for the direct solution of general second order differential equations through continuous coefficients of Linear Multi-step Method (LMM). The continuous formulation and its first derivatives were evaluated at some selected grid and off grid points to obtain our proposed method. The superiority of the method over the existing methods is established numerically.

基于遗传算法的划分序乘积空间问题求解层选择

划分序乘积空间作为一种新的粒计算模型,可以从多个视角和多个层次对问题进行描述和求解.其解空间是由多个问题求解层组成的格结构,其中每个问题求解层由多个单层次视角构成.如何在划分序乘积空间中选择问题求解层是一个NP难问题.为此,提出一种两阶段自适应遗传算法TSAGA(two stage adaptive genetic algorithm)来寻找问题求解层.首先,采用实数编码对问题求解层进行编码,然后根据问题求解层的分类精度和粒度定义适应度函数.算法第1阶段基于经典遗传算法,预选出一些优秀问题求解层作为第2阶段初始种群的一部分,从而优化解空间.算法第2阶段,提出随当前种群进化迭代次数动态变化的自适应选择算子、自适应交叉算子以及自适应大变异算子,从而在优化的解空间中进一步选择问题求解层.实验结果证明了所提方法的有效性.

顾及特征离散程度的SEaTH特征优化选择方法

特征选择是面向对象信息提取的关键步骤之一。本文针对分离阈值(separability and threshold,SEaTH)这一特征选择方法在实际应用中存在的不足,例如未考虑特征值的离散程度,仅利用J-M距离评判单一特征,特征间可能存在较强相关性,以及无法有效确定出分类顺序,提出了一种改进的SEaTH算法(optimized SEaTH,OPSEaTH)。OPSEaTH算法首先在J-M距离基础上构建了一类特征评价指标(E值),有效解决了特征值的离散度问题;然后,基于E值构建出特征组合评价指标(C_(e)值),可有效评估得到每种地物的最佳特征组合并自动确定出地物的分类顺序;最后基于eCognition等分类器可完成对地物对象的最终有效分类。利用高分二号遥感影像数据对本文方法进行了测试,并将结果分别与SEaTH算法、DPC、OIF和最近邻分类器的分类结果进行了对比,结果表明:OPSEaTH算法不仅能有效降低特征维数、优化特征空间,还能够对分类顺序进行自动化合理确定,总体精度和Kappa系数及其他精度指标,均显著优于基于SEaTH算法的特征选择结果。本文方法无论从特征降维效果、分类结果精度还是计算效率方面均优于DPC、OIF和最近邻分类器结果。OPSEaTH是一种更优的特征选择方法。

外卖智能派单的订单-骑手多目标匹配模型及其适应性算法研究

随着外卖业的快速发展,外卖智能派单成为外卖服务的重要形式。然而,目前相关研究侧重于研究外卖配送路径问题,不利于解决外卖智能派单的多目标动态决策中存在的问题。本文从外卖订单-骑手匹配的角度,分析了外卖平台智能派单的五个基本目标和四个基本约束,构建了多目标的订单-骑手匹配优化模型,进而给出了基于适应性策略的实时优化算法。最后,本文设计了外卖智能派单的仿真实验,对比了骑手-订单数量均衡、骑手供过于求、骑手供不应求等三种情况下的优化情况,验证了基于适应性策略的实时优化算法的优化效果。本研究对外卖平台发展智能派单模式具有较好的理论与实践价值。

基于高阶累积量ESPRIT算法的指数衰减正弦信号参数估计

工程应用中环境噪声多表现为高斯有色噪声,而针对高斯白噪声进行处理的算法失效问题,提出了一种高斯色噪声环境中用于多分量衰减正弦信号频率和衰减因子估计的四阶累积量ESPRIT算法。首先,推导出四阶累积量与观测样本中的自相关矩阵和互相关矩阵之间的关系,求出其四阶累积量矩阵。其次,通过对四阶累积量进行广义特征值分解,根据广义特征值即可得到信号衰减因子和频率的估计值。最后对所提算法进行了仿真实验验证,在混合信噪比为0 dB时,所提算法针对多分量衰减正弦信号角频率和衰减因子的平均估计误差分别为0.002 0πrad和0.002 0。在高斯白噪声和高斯色噪声背景下与ESPRIT算法和Prony算法相比具有更强的噪声抑制能力和更高的估计精度。

k阶采样和图注意力网络的知识图谱表示模型

知识图谱表示(KGE)旨在将知识图谱中的实体和关系映射到低维度向量空间而获得其向量表示。现有的KGE模型只考虑一阶近邻,这影响了知识图谱中推理和预测任务的准确性。为了解决这一问题,提出了一种基于k阶采样算法和图注意力网络的KGE模型。k阶采样算法通过聚集剪枝子图中的k阶邻域来获取中心实体的邻居特征。引入图注意力网络来学习中心实体邻居的注意力值,通过邻居特征加权和得到新的实体向量表示。利用ConvKB作为解码器来分析三元组的全局表示特征。在WN18RR、FB15k-237、NELL-995、Kinship数据集上的评价实验表明,该模型在链接预测任务上的性能明显优于最新的模型。此外,还讨论了阶数k和采样系数b的改变对模型命中率的影响。

超高速飞行器自适应模糊超螺旋控制律设计

针对超高速飞行器多源扰动下高精强鲁棒控制需求,提出了一种自适应模糊超螺旋控制律。首先,给出控制导向的超高速飞行器姿态模型;其次,采用广义超螺旋算法设计控制器,实现滑模变量与跟踪误差的高精收敛;同时,引入高阶齐次观测器估计飞行器复杂扰动,并在控制律中做前馈补偿以提高算法鲁棒性。针对控制增益选取问题,基于模糊算法,通过融合专家经验,建立控制增益与滑模变量直接关联。相较传统自适应方法,该自适应律可实现控制增益快速调节整定,同时兼顾系统响应速度、鲁棒性与抖振抑制需求。最后,以考虑复杂多源干扰的超高速飞行器为对象进行仿真,结果证明了自适应模糊超螺旋控制律的有效性。