This study presents an enhanced Jaya algorithm for addressing distributed no-wait flowshop scheduling problems with the objective of minimizing the maximum completion time (makespan). First, a novel hybrid initialization strategy is proposed, integrating the Nawaz-Enscore-Ham (NEH) heuristic with a problem-specific job allocation mechanism to generate a high-quality initial population. To maintain population diversity and enhance solution quality during evolution, the algorithm employs a dynamic combination of insertion and swap operators, complemented by a jigsaw puzzle-inspired algorithm recombination mechanism. Further, an acceleration strategy is introduced to expedite convergence, while a tailored local search procedure, leveraging problemspecific knowledge, is embedded to intensify exploitation capabilities. Finally, the simulation results are compared with the existing algorithms to verify the effectiveness of the proposed algorithm.

This study focuses on distributed no-wait permutation flow shop scheduling problems that have many practical engineering backgrounds. The objective is to dispatch jobs optimally to multiple processing centers and ordering them for minimizing the maximum completion time (makespan). First, to solve the problems, a mathematical model is established. Second, a novel evolutionary algorithm is proposed, in which a two-dimensional (2-D) array is designed for solution representation. Based on the problem-specific knowledge, a factory assign strategy and jigsaw puzzle inspired algorithm (JPA) are employed for initializing the population of the evolutionary algorithm. Furthermore, a relative local search is used to improve the performance of the proposed algorithm. Finally, 120 instances with different scales are solved and the results are recorded. Comparisons and discussions show the proposed algorithm has computational competitiveness in solving the concerned problems with makespan criteria.

A distributed flow-shop scheduling problem with lot-streaming that considers completion time and total energy consumption is addressed. It requires to optimally assign jobs to multiple distributed factories and, at the same time, sequence them. A biobjective mathematic model is first developed to describe the considered problem. Then, an improved Jaya algorithm is proposed to solve it. The Nawaz–Enscore–Ham (NEH) initializing rule, a job-factory assignment strategy, the improved strategies for makespan and energy efficiency are designed based on the problem’s characteristic to improve the Jaya’s performance. Finally, experiments are carried out on 120 instances of 12 scales. The performance of the improved strategies is verified. Comparisons and discussions show that the Jaya algorithm improved by the designed strategies is highly competitive for solving the considered problem with makespan and total energy consumption criteria.

This study addresses a distributed assembly permutation flowshop scheduling problem, which is of great sig-nificance in practical manufacturing systems. We aim to sequence products and jobs, and assign jobs to the appropriate factory to minimize the total flowtime of products. First, a mathematical model is developed to describe the concerned problems. Then, four meta-heuristics, e.g., artificial bee colony, particle swarm optimi-zation, genetic algorithm, and Jaya algorithm, and their variants are proposed. Three initialization strategies are developed to generate high-quality initial solutions. Four local search operators are designed to improve the performance of the algorithms. Q-learning is embedded to select the premium local search strategy during it-erations. Based on 81 large-scale benchmark instances, comprehensive numerical experiments are carried out to evaluate the effectiveness of the proposed algorithms. The experimental results show that the proposed Jaya with Q-learning-based local search has strong competitiveness, and it updates optimal solutions for 51 out of 81 benchmark instances.

This paper addresses a distributed lot-streaming permutation flow shop scheduling problem that has various applications in real-life manufacturing systems. We aim to optimally assign jobs to multiple distributed factories and sequence them to minimize the maximum completion time (Makespan). A mathematic model is first developed to describe the considered problem. Then, five meta-heuristics are executed to solve it, including particle swarm optimization, genetic algorithm, harmony search, artificial bee colony, and Jaya algorithm. To improve the performance of these meta-heuristics, we employ Nawaz-Enscore-Ham (NEH) heuristic to initialize populations and propose improved strategies based on the problem’s feature. Finally, experiments are carried out based on 120 instances. The performance of improved strategies is verified. Comparisons and discussions show that the artificial bee colony algorithm with improved strategies has the best competitiveness for solving the proposed problem with makespan criteria. Note to Practitioners—In contemporary manufacturing industry, the traditional single-factory environment is being replaced by a distributed multi-factory environment, as a distributed pattern can effectively improve the production efficiency through the reasonable resource allocation strategies. The distributed lot-streaming permutation flow shop scheduling problem in such a pattern is of significance to practitioners. Although intelligent optimization can provide an effective tool to solve such problems, most of the algorithms are parameter-sensitive. A challenge for engineers is parameter selection, which greatly impacts the algorithm performance. To ensure the robustness of the algorithms, we develop five improved meta-heuristics by employing some strategies. Furthermore, parameter setting test is carried out to select the appropriate parameter values. As a result, the proposed algorithms can obtain resource allocation schemes with high-quality. It is shown that the artificial bee colony algorithm with improved strategies outperforms other algorithms well. The proposed methodology can be readily applied to real distributed scheduling problems.

In order to solve the problem of long scheduling time and low efficiency of interval number representation scheduling method, a distributed replacement Pipeline Intelligent Scheduling Based on hybrid discrete Drosophila optimization algorithm is proposed. According to the distributed permutation pipeline scheduling problem, the coding method based on operation is adopted to make the algorithm suitable for solving the scheduling problem. The hybrid discrete Drosophila optimization algorithm is used to solve the batch pipeline scheduling problem with the maximum completion time as the goal. In order to balance the local search ability of the algorithm, the evolutionary mechanism is combined with cooperative learning among groups. Build a mathematical model to achieve efficient scheduling in the maximum completion time. The simulation results show that the scheduling time of this method is short, and the overall scheduling efficiency is higher than 80

The traditional auxiliary scheduling system in the multimedia auxiliary scheduling work, the scheduling scope is very small, resulting in poor scheduling effect. In order to solve this problem, a new distributed hybrid pipelined multimedia assistant scheduling system is designed, and the hardware and software of the system are designed respectively. The hardware part mainly designs the collector, controller and processor. The heater selects the TSDA-523 as the new type to improve the acquisition effect. The controller adopts the LPI controller to realize continuous control. The processor selects the AN176 as the central integrated processor to process a large amount of multimedia data in a short time. The software is composed of information collection, information processing and information dispatching. In order to detect the effect of the system, compared with the traditional system, the results show that the system can achieve large-scale scheduling, and effectively improve the scheduling effect.

古文字作为中国上下五千年以来的使用文字,记录了我国从古至今的文化发展历史,对于我国的历史文化研究具有十分重要的作用.对古文字的识别能够将那些珍贵的文献材料转换为电子文档,便于这些珍贵文献材料的保存和传播.该文将深度学习中经典的卷积神经网络技术应用到古文字识别中,剖析了运用的卷积神经网络技术的原理结构,并阐述了系统在识别方面所运用的技术.

在企业生产经营活动中,生产计划是最重要的依据,而生产计划是由调度系统来实施完成的,批量流水线调度问题是一个合理分配资源的过程,从而达到优化一个或多个目标的目的.优化的批量流水线调度方案,能提高企业的生产效率,并在一定程度上降低生产成本,目前的批量流水线调度方案存在一定问题,笔者提出基于新型分布式算法的批量流水线调度方法.

目的 为了更好地规划出最优可行路径.方法 采用三层规划模型(TLP)改进的三维实时路径规划算法,首先从AGV的控制性能入手,基于现实场景的多重约束设定威胁等级函数,利用TLP和改进RRT优化算法规划出更加平滑的最优路径.结果 目标函数及TLP的决策变量可以确保AGV准确到达目标位置.结论 实验结果表明,所提算法具有更好的效率与效果.

This paper presents a novel migrating birds optimization (NMBO) algorithm for solving the lot-streaming flowshop scheduling problem with minimizing makespan. The proposed NMBO algorithm utilizes discrete job permutations to represent solutions, and applies multiple neighborhoods based on insert and swap operators to improve the leading solution. Two new crossover operators, i.e., similar job order with artificial chromosome crossover, and similar block order crossover are employed to obtain solutions for the rest migrating birds. An initialization scheme based on the problem-specific heuristics is presented to generate an initial population with a certain level of quality and diversity. A local search based on the insert neighborhood is embedded to improve the algorithm’s local exploitation ability. NMBO is compared with the existing discrete invasive weed optimization, estimation of distribution algorithm and modified MBO algorithms based on the well-known lot-streaming flow shop benchmark. The computational results and comparison demonstrate the superiority of the proposed NMBO algorithm for the lot-streaming flow shop scheduling problems with makespan criterion.

流水线生产调度需要根据生产目标和各项生产约束条件,为每一加工对象规划具体的加工路径、时间、加工机器和具体操作等.生产调度作为中观层面,在管控之间发挥着承上启下的作用.当前大多数的生产调度主要依靠人工排程,在大规模生产作业中无法保证调度序列的最优化,由此使得流水线调度产生了一系列问题.基于此,在考虑行为特征的前提下,对分布式流水线调度问题进行研究,希望能够为企业生产实现资源利用最大化提供一些理论上的启发.

该文就计算机公共课上一些教学内容、教学方法、教学模式以及考试等方面问题着手,研究分析新形势下计算机教学改革下优化设计出行之有效的措施,从而在实际的教学过程中获得较好的效果.

为了提高对激光网络的故障诊断能力,需要对故障数据的弱关联特征进行有效挖掘,提出一种基于自相关功率谱密度估计的激光网络故障数据的弱关联挖掘技术.首先采用奈奎斯特采样方法进行激光网络传输数据的时间序列采样,对采集的样本数据进行短时傅里叶变换处理,实现采样数据的时频域展开.然后在时频域内对采样数据进行自相关功率谱密度估计,实现故障数据的弱关联特征挖掘.最后进行仿真测试,结果表明,采用本文方法进行故障数据挖掘,能有效实现激光网络故障检测和诊断,对目标故障数据的准确挖掘性能较好.

To solve the multi-objective hybrid flow shop scheduling problems,an improved MOEA/D algorithm was presented.Three objectives were minimized simultaneously,i.e.,the minimization of the maximal completion time,earliness penalty,and tardiness penalty.A sequence-based coding mechanism was introduced.To increase the performance,two types of local search approaches were developed.A global search based crossover operator was presented.A population update mechanism was designed to further enhance the population diversity capabilities.Based on the steelmaking production reality,20 instances were randomly generated.Compared with two recently published efficient algorithms on the 20 instances,the superiority of the proposed algorithm is verified.

为了推动基于计算思维的大学计算机基础课程的完善,培养学生的计算思维能力,文章首先阐述了计算思维的相关内容,其次对大学计算机基础课程的现状进行了分析,最后提出了基于计算思维的大学计算机基础课程改革路径.

烟花算法是一类新型的智能优化的算法,这种算法是对烟花在空中爆炸产生火花的过程进行模拟.烟花算法在求解的过程中,要有两个充分条件,其一是产生爆炸火花,在算法的布局的过程中实现全局和局部的优化,其二是产生高斯变异火花,从而实现种群的多样性,确保优良的个体可以得到遗传.分析不同的参数对算法的求解能力产生的影响,从而在车间调度的过程中可以产生最优的参数.在对作业车间调度分析的基础上,采用仿真实验的方式,确保算法求解的准确性和稳定性.

In order to solve the optimal selection problem of logistics distribution path under the increasingly congested traffic situation ,a new method of intelligent logistics distribution path optimization based on improved ant colony algorithm is proposed .Firstly ,the traditional idea of shortest path optimiza-tion based on single path is extended .The optimal path quality evaluation function based on multiple constraints is proposed ,and the optimal path model under different constraints is derived and analyzed . Secondly ,the state transition heuristic function and pheromone of the traditional ant colony algorithm are improved based on the multi constraints ,and the dynamic optimization performance of the algorithm is improved better .The computer simulation results show that the proposed method can improve the optimi-zation precision and convergence speed of the optimal path under complicated traffic conditions ,and has a good prospect of engineering application .

This paper presents a fruit fly optimization algorithm(FOA)based on dynamic double subgroup for solving the Bi-criteria No-wait Flowshop Scheduling Problem(BNFSP)with makespan and idle time criteria. Unlike the traditional FOA, the proposed algorithm applies the job-permutation-based representation and initialization method based on the improved NEH(Nawaz-Enscore-Ham). Secondly, the whole group is dynamically divided into advanced subgroup and backward subgroup according to its own evolutionary level. A simple but effective insert search algorithm is made for advanced subgroup in the neighborhood, and iterative greedy is made for backward subgroup, so that the who group keeps in good balance between global exploration and local exploitation. Finally, to improve the efficiency of the scheduling algorithm, several speed-up methods are devised to evaluate a job permutation and its whole insert neighborhood as well as to decide the domination status of a solution with the archive set. Computational results show that the FOA presented in this paper is very effective and efficient for the BNFSP .

文章针对计算机基础教学过程中遇到的问题进行分析,分别从教学内容设置、教学方法创新和分层教学几个方面提出整改措施,以期逐步提高教学质量,使学生能够利用计算机解决专业问题,为社会培养应用型人才.