An improved multi-objective optimization algorithm for solving flexible job shop scheduling problem with variable batches

In order to solve the flexible job shop scheduling problem with variable batches,we propose an improved multiobjective optimization algorithm,which combines the idea of inverse scheduling.First,a flexible job shop problem with the variable batches scheduling model is formulated.Second,we propose a batch optimization algorithm with inverse scheduling in which the batch size is adjusted by the dynamic feedback batch adjusting method.Moreover,in order to increase the diversity of the population,two methods are developed.One is the threshold to control the neighborhood updating,and the other is the dynamic clustering algorithm to update the population.Finally,a group of experiments are carried out.The results show that the improved multi-objective optimization algorithm can ensure the diversity of Pareto solutions effectively,and has effective performance in solving the flexible job shop scheduling problem with variable batches.

A Genetic Algorithm-based Approach to Scheduling of Batch Production with Maximum Profit

The optimal scheduling of multi-product batch process is studied and a new mathematics model targeting the maximum profit is proposed, which can be solved by the modified genetic algorithm (MGA) with mixed coding (sequence coding and decimal coding) developed by us. In which, the partially matched cross over (PMX) and reverse mutation are used for the sequence coding, whereas the arithmetic crossover and heteropic mutation are used for the decimal coding. In axidition, the relationship between production scale and production cost is analyzed and the maximum profit is always a trade-off of the production scale and production cost. Two examples are solved to demonstrate the effectiveness of the method.

A Practical SSL Server Performance Improvement Algorithm Based on Batch RSA Decryption

The secure socket layer/ transport layer security(SSL/TLS) handshake protocol uses public key cryptographic algorithms such as RSA for key establishment. Typically, public key cryptographic algorithm is computational intensive due to the modular multiplications. Therefore, SSL/TLS servers often become swamped while performing public key decryptions when the simultaneous requests increase quickly. A batch RSA decryption algorithm was proposed. The novel algorithm provides the reasonable response time and optimizes server performance significantly. The decryption speedup is proportional to the batch size 6, for instance, the speedup factor is 4, while in Shacham's scheme the acceleration rate is only 2.5 when 6 = 4.

A Note on DP Algorithm for Batching Scheduling to Minimize Maximum Lateness

In parallel-batching machine scheduling, all jobs in a batch start and complete at the same time, and the processing time of the batch is the maximum processing time of any job in it. For the unbounded parallel-batching machine scheduling problem of minimizing the maximum lateness, denoted 1|p-batch|L_(max), a dynamic programming algorithm with time complexity O(n^2) is well known in the literature.Later, this algorithm is improved to be an O(n log n) algorithm. In this note, we present another O(n log n) algorithm with simplifications on data structure and implementation details.

Hierarchical On-line Scheduling of Multiproduct Batch Plants with a Combined Approach of Mathematical Programming and Genetic Algorithm

In this contribution we present an online scheduling algorithm for a real world multiproduct batch plant. The overall mixed integer nonlinear programming (MINLP) problem is hierarchically structured into a mixed integer linear programming (MILP) problem first and then a reduced dimensional MINLP problem, which are optimized by mathematical programming (MP) and genetic algorithm (GA) respectively. The basis idea relies on combining MP with GA to exploit their complementary capacity. The key features of the hierarchical model are explained and illustrated with some real world cases from the multiproduct batch plants.

基于MOMA的可重入混合流水车间调度问题研究

针对可重入制造系统多具有多品种、大规模、混流生产等特点,构建带批处理机的可重入混合流水车间调度问题(reentrant hybrid flow shop scheduling problem with batch processors,BPRHFSP)模型,提出一种改进的多目标蜉蝣算法(multi-objective mayfly algorithm,MOMA)进行求解。提出了单件加工阶段和批处理阶段的解码规则;设计了基于Logistic混沌映射的反向学习初始化策略、改进的蜉蝣交配和变异策略,提高了算法初始解的质量和局部搜索能力;根据编码规则设计了基于变邻域下降搜索的蜉蝣运动策略,优化了种群方向。通过对不同规模大量测试算例的仿真实验,验证了MOMA相比传统算法求解BP-RHFSP更具有效性和优越性。所提出的模型能够反映生产的基础特征,达到减少最大完工时间、机器负载和碳排放的目的。

基于国密SM2算法的车联网无证书隐私保护认证协议

作为大多数智能交通系统的基础,车载自组网(Vehicular Ad-Hoc Network,VANET)能够有效改善交通管理和提升道路安全性,因此受到广泛关注。然而,由于无线通信环境的开放性,VANET中的各个节点易遭受隐私泄露攻击。许多条件隐私保护认证(Conditional Privacy-Preserving Authentication,CPPA)协议被提出来解决隐私泄露问题,但这些协议主要基于传统的签名算法,这使得它们存在复杂的证书管理或密钥托管问题。为解决以上问题,本文以国密SM2签名算法为基础提出了一种适合VANET环境中无证书条件隐私保护认证协议,该协议在降低了通信开销的同时也避免了证书管理和密钥托管问题。此外,协议还保证了当出现恶意车辆用户时,可信中心可以追踪用户的真实身份并撤销该用户。安全证明和分析表明该协议具有较高的安全性并且满足消息完整性、匿名性、可追踪性和可撤销性等安全要求。最后,与最近的三种同类隐私协议对比,仿真实验结果表明该协议在通信量和计算量上优于其他方案。

批式流加发酵中的鲁棒脉冲时滞最优控制

本文研究了批式流加发酵中的鲁棒脉冲时滞最优控制问题。首先,提出一个非线性状态依赖的脉冲时滞系统描述批式流加发酵甘油生产1,3-丙二醇(1,3-PD)过程。由于批式流加发酵过程中的动力学参数难以准确估计,本文建立了一个具有连续状态不等式约束的鲁棒脉冲时滞最优控制模型。这里,目标函数为终端时刻1,3-PD浓度及其关于动力学参数的灵敏性的加权和,控制向量为流加发生时甘油的临界浓度及每次流加时甘油的流加体积。然后,通过引入辅助脉冲系统,将该鲁棒最优控制问题转化为等价的标准最优控制问题。进一步,通过约束转换技术,将等价的最优控制问题转化为仅具有盒式约束的罚问题。最后,设计了一种并行差分进化算法求解转化后的罚问题。数值结果表明:当参数受到微小扰动时,尽管牺牲了少量的1,3-PD浓度,但是明显提高了系统的鲁棒性。

基于改进遗传算法的混合流水车间批量调度问题求解

针对多品种小批量混流生产模式中生产计划调度复杂的特点,提出解决批量问题的等量分批策略,实现工件在不同工序上同时加工,缩减机器等待时间;以最大完工时间为优化目标,建立混合流水车间批量调度问题数学模型;设计求解模型的改进遗传算法,使用NEH启发式算法和随机生成结合的方式生成优质初始解,采用二元锦标赛进行选择操作,采用二元交叉法进行交叉操作,采用插入变异生成新个体,并使用贪婪插入的领域搜索算法进行局部搜索,解码时采用“子批优先+先空闲先加工”策略。发动机连杆生产案例应用结果表明,混合流水车间批量调度问题模型与改进的遗传算法正确有效。

双区型仓库订单分批与拣选协同优化研究

针对订单分拣效率低下导致商品出库缓慢的问题,提出一种基于双区型仓库订单分批与拣选的协同优化模型,设计求解模型的CWDP-BSA(clarke-wright and dynamic programming&backtracking search algorithm)协同优化算法。在节约算法中引入快速排序法对订单组合的距离节约值排序,考虑AGV承载量,运用多阶段决策过程最优策略得出状态转移方程求解订单分批模型,确定初始分批方案;并采取多因子选择的回溯搜索算法求解拣选路径模型,以此确定初始拣选方案。再以以上两方案为基础,建立新的基于订单时间窗的订单分批和拣选协同优化模型并求解,进一步优化订单分批和拣选方案。最后通过对比实验得出,平均每批次订单的拣选距离减少了约24.56%,优化后的拣选时间比优化前缩短了约11.4%,在求解不同规模算例时,CWDP-BSA算法的求解结果优于CPLEX软件和其他算法,验证了模型与算法的稳定性和有效性。实验表明,协同优化后的订单分批与物品拣选策略能够有效提升订单出库效率。

基于人工鱼群-遗传算法的多品种小批量零件数控加工工艺优化研究

基于多品种小批量零件加工成本高的问题,基于人工鱼群-遗传算法(AFSA-GA)构建了数控机床能耗模型,以实现零件加工能耗下降。首先,将数控机床功率划分为各工序功率模型,基于功率模型与工作时间关系得出机床运转能耗模型,结合产品表面粗糙度模型,对各工序能耗模型及整体粗糙度进行归一化处理,形成整体能耗模型;其次,以能耗及粗糙度为目标函数,建立AFSA-GA算法,通过对各工序能耗求解得出最适当的机床功率及其所对应的能耗和表面粗糙度;最后,针对所获得的最优功率,进行优化结果的验证,为五轴机床的实际加工提供解决方案。

一种高效轻量的双参数椭圆曲线数字签名批量验证方案

为解决大规模交易任务的签名验证给区块链节点带来繁琐的开销和经典的椭圆曲线数字签名算法(ECDSA)中耗时的模逆运算,提出一种高效轻量的双参数椭圆曲线数字签名(TP-ECDSA)批量验证方案。一方面,TP-ECDSA在签名和验证阶段均无模逆运算,在该算法中引入批量验证能将标量乘的计算次数从2t减少到[2,t+1]。另一方面,KGLP算法能加速标量乘运算,引入该算法能显著地提升签名验证的效率。实验表明,与ECDSA相比,不论是独立验证还是批量验证,TP-ECDSA的运行速度均提高了50%以上,签名验证效率显著提升。

Optimized air-ground data fusion method for mine slope modeling

Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized characteristics of mining slopes,this study introduces a new method that fuses model data from Unmanned aerial vehicles(UAV)tilt photogrammetry and 3D laser scanning through a data alignment algorithm based on control points.First,the mini batch K-Medoids algorithm is utilized to cluster the point cloud data from ground 3D laser scanning.Then,the elbow rule is applied to determine the optimal cluster number(K0),and the feature points are extracted.Next,the nearest neighbor point algorithm is employed to match the feature points obtained from UAV tilt photogrammetry,and the internal point coordinates are adjusted through the distanceweighted average to construct a 3D model.Finally,by integrating an engineering case study,the K0 value is determined to be 8,with a matching accuracy between the two model datasets ranging from 0.0669 to 1.0373 mm.Therefore,compared with the modeling method utilizing K-medoids clustering algorithm,the new modeling method significantly enhances the computational efficiency,the accuracy of selecting the optimal number of feature points in 3D laser scanning,and the precision of the 3D model derived from UAV tilt photogrammetry.This method provides a research foundation for constructing mine slope model.

基于嵌套蚁群算法的机器人拣货作业联合优化

针对物流仓储中心拣货作业过程中系统订单分批和拣货路径分步拣选效率低的问题,提出一种基于嵌套蚁群的订单分批和路径优化的联合拣货策略。首先,建立以最小化总路径为目标的订单分批与拣货路径联合优化模型;然后,考虑双重优化的复杂性,设计一种嵌套蚁群算法对模型进行求解,以订单分批模型为基准不断优化订单分批结果,得出最优分批集合单,其优化集合单再嵌套蚁群算法实现拣货路径优化。为验证该算法对随机订单有效性,抽取某一天17:00-18:00时段内既有货架区货物又有地堆区货物的43个订单算例进行仿真实验,与传统订单分批和拣货路径分步拣选策略相比,基于嵌套蚁群算法的拣货作业联合优化模型的随机订单拣货路径更短、拣货时间更少,经过联合优化后,机器人总拣选距离缩短了170 m。基于嵌套蚁群算法的拣货作业联合优化模型和其求解算法可以有效解决订单分批与拣货路径联合优化问题,为配送中心拣选系统的优化提供依据。

考虑拆分策略的智能仓库订单分拣建模与优化

针对一种两类订单混合分拣的自动小车分拣问题,考虑其在订单分拣过程中出现的分拣AGV(automatic guided vehicle)堵塞和人工收集站闲置的现象,提出一种订单拆分的策略及拆分后子订单批次调整的方法,并以总订单完工时间最小化为优化目标,建立了订单拆分的订单分拣整数规划模型;提出一种改进离散灰狼优化算法,将订单分批、批次排序以及下架库位选取3个子问题进行联合优化。数值实验结果表明,订单拆分策略使总订单完工时间显著减少,并且通过与经典算法以及其他同类型算法的对比,验证了所提算法的优越性。

聚类和NSGA-Ⅱ联合算法在混合流水车间的应用研究

为了改善某高端装备制造企业总装车间混流生产调度困难、批处理阶段产品组批困难的问题,以及实现车间多个目标的同步联合优化,研究了含批处理机的混合流水车间多目标优化问题。首先根据车间运行情况建立了多目标优化模型,之后提出了基于K-means聚类算法和非支配排序遗传算法(NSGA-Ⅱ)的联合方法,设计了能够对不相容产品进行分组的聚类流程,以及基于产品组编号和组内产品编号的双层编码方式,为批处理工序设计了完整的组批流程。最后,使用车间生产案例进行测试,并将测试结果同仅使用NSGA-Ⅱ得到的结果进行对比,验证了所提方法的有效性。

多品种小批量产品的分层计划管控系统设计与实践

针对中小企业生产管理指导性、规范性差,生产作业效率低等问题,本文以A公司多品种小批量产品为研究对象,提出一整套分层计划管控系统。首先通过流程再造优化订单评审流程,其次,采用分层计划编制体系优化计划排产流程,然后构建基于规则约束的启发式算法进行优化。最终达到增强生产订单接收协同性,排产指导性以及反馈及时性的目的。

应用改进卷积神经网络的客户服务业务中台资源异常信息主动报警

针对客户服务业务中台资源异常信息人工诊断不及时、故障辨识率低等问题,提出一种基于改进卷积神经网络的故障诊断方法。卷积层后引入批量归一化层提高模型的泛化能力,在全连接层引入Droupout函数来缓解过拟合问题,还对数据进行了增强处理以及运用灰狼算法对超参数进行寻优。该模型在Pytorch和Pycharm环境下进行仿真,得出经典卷积神经网络的测试集准确率在85%左右,而改进后的测试集准确率在94%左右,表明所提设计具有明显效果。

基于改进禁忌搜索算法的药房批量取药路径规划研究

针对药房批量取药这一现实问题,在设计药房整体环境布局和引入路径规划算法的基础上,提出以重量加权的距离为目标的旅行商问题TSP(Traveling Salesman Problem)。建立一个混合整数规划模型,使用禁忌搜索算法进行模型求解,使用曼哈顿距离作为两点之间距离,在禁忌长度等参数设置上使用动态自适应方法,并在算法中加入扰动方法,避免算法陷入局部最优,增加搜索目标的多样性。最后使用JAVA进行了仿真模拟实验,可视化结果验证了算法的可行性和有效性。

基于改进灰狼优化算法的柔性作业车间分批调度问题研究

针对以最小化最大完工时间为目标的柔性作业车间分批调度问题(Flexible Job shop Batch Scheduling Problem,FJBSP),提出了一种改进灰狼优化(Improved Grey Wolf Optimization,IGWO)算法,优化对象为工件的分批方案。首次将流体模型应用于FJBSP的求解,提出了一种基于流体模型的解码方法,用于获得更好的子批调度方案;然后改进了狼群的等级制度,避免了算法过早收敛;其次设计了一种全新的适应可变长编码的交叉方法,深入交流2个个体之间的分批信息,增强了算法的搜索能力和稳定性;再次,提出了能够动态更新个体游走率的自适应灰狼游走策略,兼顾了算法的搜索质量和收敛速度;此外,对领头狼使用自适应邻域搜索动态调整每种工件选择各邻域的概率,提高算法的局部搜索能力;最后,设计了9个算例和3组实验,验证了所提出的IGWO算法的有效性和优越性。