A Cooperated Imperialist Competitive Algorithm for Unrelated Parallel Batch Machine Scheduling Problem

This study focuses on the scheduling problem of unrelated parallel batch processing machines(BPM)with release times,a scenario derived from the moulding process in a foundry.In this process,a batch is initially formed,placed in a sandbox,and then the sandbox is positioned on a BPM formoulding.The complexity of the scheduling problem increases due to the consideration of BPM capacity and sandbox volume.To minimize the makespan,a new cooperated imperialist competitive algorithm(CICA)is introduced.In CICA,the number of empires is not a parameter,and four empires aremaintained throughout the search process.Two types of assimilations are achieved:The strongest and weakest empires cooperate in their assimilation,while the remaining two empires,having a close normalization total cost,combine in their assimilation.A new form of imperialist competition is proposed to prevent insufficient competition,and the unique features of the problem are effectively utilized.Computational experiments are conducted across several instances,and a significant amount of experimental results show that the newstrategies of CICAare effective,indicating promising advantages for the considered BPMscheduling problems.

Research on Flexible Job Shop Scheduling Based on Improved Two-Layer Optimization Algorithm

To improve the productivity,the resource utilization and reduce the production cost of flexible job shops,this paper designs an improved two-layer optimization algorithm for the dual-resource scheduling optimization problem of flexible job shop considering workpiece batching.Firstly,a mathematical model is established to minimize the maximum completion time.Secondly,an improved two-layer optimization algorithm is designed:the outer layer algorithm uses an improved PSO(Particle Swarm Optimization)to solve the workpiece batching problem,and the inner layer algorithm uses an improved GA(Genetic Algorithm)to solve the dual-resource scheduling problem.Then,a rescheduling method is designed to solve the task disturbance problem,represented by machine failures,occurring in the workshop production process.Finally,the superiority and effectiveness of the improved two-layer optimization algorithm are verified by two typical cases.The case results show that the improved two-layer optimization algorithm increases the average productivity by 7.44% compared to the ordinary two-layer optimization algorithm.By setting the different numbers of AGVs(Automated Guided Vehicles)and analyzing the impact on the production cycle of the whole order,this paper uses two indicators,the maximum completion time decreasing rate and the average AGV load time,to obtain the optimal number of AGVs,which saves the cost of production while ensuring the production efficiency.This research combines the solved problem with the real production process,which improves the productivity and reduces the production cost of the flexible job shop,and provides new ideas for the subsequent research.

PCR ALGORITHM FOR PARALLEL COMPUTING MINIMUM-NORM LEAST-SQUARES SOLUTION OF INCONSISTENT LINEAR EQUATIONS

This paper presents a new highly parallel algorithm for computing the minimum-norm least-squares solution of inconsistent linear equations Ax = b(A∈Rm×n,b∈R (A)). By this algorithm the solution x = A + b is obtained in T = n(log2m + log2(n - r + 1) + 5) + log2m + 1 steps with P=mn processors when m × 2(n - 1) and with P = 2n(n - 1) processors otherwise.

NEW EFFICIENT ORDER-RECURSIVE LEAST-SQUARES ALGORITHMS

Order-recursive least-squares(ORLS)algorithms are applied to the prob-lems of estimation and identification of FIR or ARMA system parameters where a fixedset of input signal samples is available and the desired order of the underlying model isunknown.On the basis of several universal formulae for updating nonsymmetric projec-tion operators,this paper presents three kinds of LS algorithms,called nonsymmetric,symmetric and square root normalized fast ORLS algorithms,respectively.As to the au-thors’ knowledge,the first and the third have not been so far provided,and the second isone of those which have the lowest computational requirement.Several simplified versionsof the algorithms are also considered.

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 addition, 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.

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 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 b, for instance, the speedup factor is 4, while in Shacham＇s scheme the acceleration rate is only 2.5 when b = 4.

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.

Genetic algorithm for short-term scheduling of make-and-pack batch production process

This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy all constraints while meeting demand requirement of packed products from various product families. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore,we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromosomes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to determine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for comparison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, all heuristics show the capability to solve large instances within reasonable computational time. In all problem instances, genetic algorithm averagely outperforms ant colony optimization and Tabu search with slightly longer computational time.

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.

A Control Algorithm for the Optimization of Batch Reactor-Based Processes

Levenberg-Marquardt(LM)algorithm is applied for the optimization of the heat transfer of a batch reactor.The validity of the approach is verified through comparison with experimental results.It is found that the mathematical model can properly describe the heat transfer relationships characterizing the considered system,with the error being kept within±2℃.Indeed,the difference between the actual measured values and the model calculated value curve is within±1.5℃,which is in agreement with the model assumptions and demonstrates the reliability and effectiveness of the algorithm applied to the batch reactor heat transfer model.Therefore,the present work provides a theoretical reference for the conversion of practical problems in the field of chemical production into mathematical models.

Research on Optimization of Dual-Resource Batch Scheduling in Flexible Job Shop

With the rapid development of intelligent manufacturing and the changes in market demand,the current manufacturing industry presents the characteristics of multi-varieties,small batches,customization,and a short production cycle,with the whole production process having certain flexibility.In this paper,a mathematical model is established with the minimum production cycle as the optimization objective for the dual-resource batch scheduling of the flexible job shop,and an improved nested optimization algorithm is designed to solve the problem.The outer layer batch optimization problem is solved by the improved simulated annealing algorithm.The inner double resource scheduling problem is solved by the improved adaptive genetic algorithm,the double coding scheme,and the decoding scheme of Automated Guided Vehicle(AGV)scheduling based on the scheduling rules.The time consumption of collision-free paths is solved with the path planning algorithm which uses the Dijkstra algorithm based on a time window.Finally,the effectiveness of the algorithm is verified by actual cases,and the influence of AGV with different configurations on workshop production efficiency is analyzed.

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。

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。

基于最优觅食算法的增材制造中多种类零件分批排样研究

增材制造中多种类零件分批排样存在打印时间成本高和工作台空间利用率低等问题,且需考虑零件高度的影响。分批排样问题包括零件在工作台上放置策略和成型批次分配两个子问题。放置策略涉及成型方向选择、零件碰撞检测和定位策略。基于建立的成型方向准则,利用多边形表示零件投影轮廓,提出基于临界多边形的改进移动碰撞法以确定3种不同形态多边形零件的免碰撞排放范围,提出新的左下定位策略放置零件,新定位策略融合了改进的建设性方法和契合度;提出单机台面积占用最大化策略实现零件成型批次分配。基于上述研究,提出基于最优觅食算法的分批排样算法,算法采用双重编码表达零件放置顺序和旋转角度,以最小化完工时间为目标实现优化分批排样。以案例库零件为对象,与3种对比算法比较,表明所提方法的方案能有效提高增材制造的空间利用率和缩短完工时间。

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。

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。

带有动态到达工件的分布式柔性作业车间调度问题研究

分布式柔性作业车间调度是生产调度的1个重要分支,工件的动态到达作为实际生产中的1种常见扰动情况,进一步增加了作业车间调度问题的复杂性和不确定性。针对带有工件动态到达的分布式柔性作业车间调度问题(DA-DFJSP),提出1种分批调度策略,将原本的动态调度问题转化成一系列连续调度区间上的静态调度问题,构建以最大完工时间为优化目标的混合整数规划模型;在此基础上,结合问题特征采用批次、工厂、工序、机器的4层染色体编码及快速贪婪搜索插入的解码方式改进遗传算法,同时引入多种交叉、变异算子来增强染色体的多样性;最后,基于FJSP标准算例构建DA-DFJSP测试算例进行仿真对比实验,验证所提策略和改进算法的求解优势。结果表明:相较于传统的重调度策略和改进前的遗传算法,采用分批调度策略和改进的遗传算法(IGA)所求调度方案具有更短的完工周期、更均匀的工厂加工负荷及更高的设备工作效率,IGA与分批调度策略之间有高度的契合性,能够有效提升生产效率。