期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到264,344篇文章
< 1 2 … 250 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Hyper-heuristic Evolutionary Algorithm Utilizing Q-learning for Addressing the Distributed Flexible Job-Shop Scheduling Problem in the Context of Worker Absenteeism 认领 引用
1
作者 Zhiqing Li Yongquan Zhou Qifang Luo 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1932-1969,共38页
The Distributed Flexible Job Shop Scheduling Problem(DFJSP)is critical in modern manufacturing;however,existing research has not sufficiently addressed dynamic disturbances,particularly unexpected worker absences.This... The Distributed Flexible Job Shop Scheduling Problem(DFJSP)is critical in modern manufacturing;however,existing research has not sufficiently addressed dynamic disturbances,particularly unexpected worker absences.This study extends the DFJSPW model to introduce an enhanced framework,DFJSPWA,which optimizes maximum makespan,worker workload,and total energy consumption by integrating worker load factors with random absenteeism.To solve this complex problem,we propose a Q-learning-based Hyper-heuristic Evolutionary Algorithm(QLHHEA).This algorithm features a segmented encoding scheme that implicitly captures absenteeism information,utilizing a decoding process tailored for both standard and rescheduling contexts.Additionally,we construct a pool of twelve efficient Low-Level Heuristics(LLHs)combined with Q-learning to enable the adaptive selection of operators.Furthermore,a Hybrid Rescheduling Method(HRM)is developed,employing three response strategies based on worker status and the urgency of the absenteeism.Comprehensive experiments on 58 benchmark instances demonstrate that QLHHEA significantly outperforms six established algorithms,including MOEA/D and NSGA-II.Statistical validation confirms the superiority of the proposed method.This research provides a robust theoretical and methodological framework for improving scheduling efficiency and resource utilization in distributed production systems facing worker absenteeism. 展开更多
关键词 Worker absenteeism Distributed flexible job shop scheduling problem Hyper-heuristic evolutionary algorithm Dynamic rescheduling Q-learning
An Adaptive Imperialist Competitive Algorithm with Cooperation for Flexible Jobshop and Parallel Batch Processing Machine Scheduling 认领 引用
2
作者 Jie Wang Deming Lei 《Computers, Materials & Continua》 SCIE EI 2026年第6期1934-1960,共27页
Both flexible jobshop scheduling and parallel batch processing machine scheduling have been extensively considered;however,the flexible jobshop and parallel batch processing machine scheduling problem(FJPBPMSP)is prev... Both flexible jobshop scheduling and parallel batch processing machine scheduling have been extensively considered;however,the flexible jobshop and parallel batch processing machine scheduling problem(FJPBPMSP)is prevalent in real-life manufacturing processes and is seldom investigated.In this study,FJPBPMSP is examined,where flexible processing and batch processing are performed sequentially.An adaptive imperialist competitive algorithm with cooperation(CAICA)is proposed to minimize makespan and total energy consumption simultaneously.In CAICA,a four-string representation is adopted,and initial empires with novel structures are formed by uniformly dividing the population.An adaptive assimilation and revolution are designed.An adaptive assimilation and revolution are designed.An adaptive imperialist competition with cooperation is provided.Search strategies,imperialists,and colonies are also renewed by new procedures.Computational experiments are conducted on 50 instances.The computational results show that the new strategies of CAICA are effective,and CAICA can provide better results than its comparative algorithms in solving FJPBPMSP. 展开更多
关键词 Flexible jobshop scheduling parallel batch processing scheduling imperialist competition algorithm cooperation
暂未订购 下载PDF
Distributed Flexible Job Shop Scheduling Based on an Improved Genetic Algorithm 认领 引用
3
作者 Wenwen Chen Bowen Du 《Journal of Electronic Research and Application》 2026年第6期131-136,共6页
Aiming at the multi-dimensional and highly complex decision-making characteristics of distributed flexible job shop scheduling,and the local convergence and slow convergence speed of the traditional genetic algorithm,... Aiming at the multi-dimensional and highly complex decision-making characteristics of distributed flexible job shop scheduling,and the local convergence and slow convergence speed of the traditional genetic algorithm,an improved genetic algorithm-the dynamic hierarchical genetic algorithm is proposed.The algorithm enhances the global search ability and local optimization ability of the traditional genetic algorithm through a dynamic hierarchical structure,a multi-neighborhood local search strategy,and adaptive adjustment of the crossover and mutation rates.The simulation results show that after 10 independent runs on several improved Kacem(MK)series test cases,the algorithm has a lower minimum average maximum completion time than the traditional genetic algorithm.Especially in the complex cases of MK03 and MK09,not only is the completion time reduced by 24%,but also the number of iterations is reduced.This fully verifies the ability of the algorithm to improve scheduling efficiency and stability in distributed flexible manufacturing workshops.The completion time of MK09 is reduced by about 24%,and the number of iterations is also reduced.This fully verifies the ability of the algorithm to improve the scheduling efficiency and stability in the distributed flexible manufacturing workshop scheduling problem. 展开更多
关键词 Distributed flexible job shop scheduling Dynamic population classification Multi-neighborhood local search
暂未订购 下载PDF
Algorithmic opacity and employees’knowledge hiding:medication by job insecurity and moderation by employee-AI collaboration 认领 引用
4
作者 Chunhong Guo Huifang Liu Jingfu Guo 《Journal of Psychology in Africa》 2025年第3期411-418,共8页
We explored the effects of algorithmic opacity on employees’playing dumb and evasive hiding rather than rationalized hiding.We examined the mediating role of job insecurity and the moderating role of employee-AI coll... We explored the effects of algorithmic opacity on employees’playing dumb and evasive hiding rather than rationalized hiding.We examined the mediating role of job insecurity and the moderating role of employee-AI collaboration.Participants were 421 full-time employees(female=46.32%,junior employees=31.83%)from a variety of organizations and industries that interact with AI.Employees filled out data on algorithm opacity,job insecurity,knowledge hiding,employee-AI collaboration,and control variables.The results of the structural equation modeling indicated that algorithm opacity exacerbated employees’job insecurity,and job insecurity mediated between algorithm opacity and playing dumb and evasive hiding rather than rationalized hiding.The relationship between algorithmic opacity and playing dumb and evasive hiding was more positive when the level of employee-AI collaboration was higher.These findings suggest that employee-AI collaboration reinforces the indirect relationship between algorithmic opacity and playing dumb and evasive hiding.Our study contributes to research on human and AI collaboration by exploring the dark side of employee-AI collaboration. 展开更多
关键词 algorithmic opacity job insecurity knowledge hiding employee-AI collaboration
暂未订购 下载PDF
A Q-Learning-Assisted Co-Evolutionary Algorithm for Distributed Assembly Flexible Job Shop Scheduling Problems 认领 引用
5
作者 Song Gao Shixin Liu 《Computers, Materials & Continua》 SCIE EI 2025年第6期5623-5641,共19页
With the development of economic globalization,distributedmanufacturing is becomingmore andmore prevalent.Recently,integrated scheduling of distributed production and assembly has captured much concern.This research s... With the development of economic globalization,distributedmanufacturing is becomingmore andmore prevalent.Recently,integrated scheduling of distributed production and assembly has captured much concern.This research studies a distributed flexible job shop scheduling problem with assembly operations.Firstly,a mixed integer programming model is formulated to minimize the maximum completion time.Secondly,a Q-learning-assisted coevolutionary algorithmis presented to solve themodel:(1)Multiple populations are developed to seek required decisions simultaneously;(2)An encoding and decoding method based on problem features is applied to represent individuals;(3)A hybrid approach of heuristic rules and random methods is employed to acquire a high-quality population;(4)Three evolutionary strategies having crossover and mutation methods are adopted to enhance exploration capabilities;(5)Three neighborhood structures based on problem features are constructed,and a Q-learning-based iterative local search method is devised to improve exploitation abilities.The Q-learning approach is applied to intelligently select better neighborhood structures.Finally,a group of instances is constructed to perform comparison experiments.The effectiveness of the Q-learning approach is verified by comparing the developed algorithm with its variant without the Q-learning method.Three renowned meta-heuristic algorithms are used in comparison with the developed algorithm.The comparison results demonstrate that the designed method exhibits better performance in coping with the formulated problem. 展开更多
关键词 Distributed manufacturing flexible job shop scheduling problem assembly operation co-evolutionary algorithm Q-learning method
暂未订购 下载PDF
An Effective Local Search Algorithm for Flexible Job Shop Scheduling in Intelligent Manufacturing Systems 认领 引用 被引量：3
6
作者 Junjie Zhang Zhipeng Lü +3 位作者 Junwen Ding Zhouxing Su Xinyu Li Liang Gao 《Engineering》 SCIE EI CSCD 2025年第7期117-127,共11页
As one of the most classical scheduling problems,flexible job shop scheduling problems(FJSP)find widespread applications in modern intelligent manufacturing systems.However,the majority of meta-heuristic methods for s... As one of the most classical scheduling problems,flexible job shop scheduling problems(FJSP)find widespread applications in modern intelligent manufacturing systems.However,the majority of meta-heuristic methods for solving FJSP in the literature are population-based evolutionary algorithms,which are complex and time-consuming.In this paper,we propose a fast effective singlesolution based local search algorithm with an innovative adaptive weighting-based local search(AWLS)technique for solving FJSP.The adaptive weighting technique assigns weights to each operation and adaptively updates them during the exploration.AWLS integrates a Tabu Search strategy and the adaptive weighting technique to smooth the landscape of the search space and enhance the exploration diversity.Computational experiments on 313 well-known benchmark instances demonstrate that AWLS is highly competitive with state-of-the-art algorithms in terms of both solution quality and computational efficiency,despite of its simplicity.Specifically,AWLS improves the previous best-known results in the literature on 33 instances and match the best-known results on the remaining ones except for only one under the same time limit of up to 300 s.As a strongly non-deterministic polynomia(NP)-hard problem which has been extensively studied for nearly half a century,breaking the records on these classic instances is an arduous task.Nevertheless,AWLS establishes new records on 8 challenging instances whose previous best records were established by a state-of-the-art meta-heuristic algorithm and a famous industrial solver. 展开更多
关键词 Job shop scheduling Adaptive weighting technique Intelligent manufacturing systems
暂未订购 下载PDF
Solving flexible job shop scheduling problem by a multi-swarm collaborative genetic algorithm 认领 引用 被引量：16
7
作者 WANG Cuiyu LI Yang LI Xinyu 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2021年第2期261-271,共11页
The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborativ... The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborative optimization algorithm is proposed for the FJSP.Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA.Good operators are adopted and designed to ensure this algorithm to achieve a good performance.Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA.The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms. 展开更多
关键词 flexible job shop scheduling problem(FJSP) collaborative genetic algorithm co-evolutionary algorithm
暂未订购 下载PDF
An improved multi-objective optimization algorithm for solving flexible job shop scheduling problem with variable batches 认领 引用 被引量：5
8
作者 WU Xiuli PENG Junjian +2 位作者 XIE Zirun ZHAO Ning WU Shaomin 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2021年第2期272-285,共14页
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 pro... 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. 展开更多
关键词 flexible job shop variable batch inverse scheduling multi-objective evolutionary algorithm based on decomposition a batch optimization algorithm with inverse scheduling
暂未订购 下载PDF
SOLVING FLEXIBLE JOB SHOP SCHEDULING PROBLEM BY GENETIC ALGORITHM 认领 引用 被引量：12
9
作者 乔兵 孙志峻 朱剑英 《Transactions of Nanjing University of Aeronautics and Astronautics》 2001年第1期108-112,共5页
The job shop scheduli ng problem has been studied for decades and known as an NP-hard problem. The fl exible job shop scheduling problem is a generalization of the classical job sche duling problem that allows an oper... The job shop scheduli ng problem has been studied for decades and known as an NP-hard problem. The fl exible job shop scheduling problem is a generalization of the classical job sche duling problem that allows an operation to be processed on one machine out of a set of machines. The problem is to assign each operation to a machine and find a sequence for the operations on the machine in order that the maximal completion time of all operations is minimized. A genetic algorithm is used to solve the f lexible job shop scheduling problem. A novel gene coding method aiming at job sh op problem is introduced which is intuitive and does not need repairing process to validate the gene. Computer simulations are carried out and the results show the effectiveness of the proposed algorithm. 展开更多
关键词 flexible job shop gene tic algorithm job shop scheduling
暂未订购 下载PDF
INTEGRATED OPERATOR GENETIC ALGORITHM FOR SOLVING MULTI-OBJECTIVE FLEXIBLE JOB-SHOP SCHEDULING 认领 引用 被引量：1
10
作者 袁坤 朱剑英 +1 位作者 鞠全勇 王有远 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2006年第4期278-282,共5页
In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objectiv... In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objective FJSP, the Grantt graph oriented string representation （GOSR） and the basic manipulation of the genetic algorithm operator are presented. An integrated operator genetic algorithm （IOGA） and its process are described. Comparison between computational results and the latest research shows that the proposed algorithm is effective in reducing the total workload of all machines, the makespan and the critical machine workload. 展开更多
关键词 flexible job-shop integrated operator genetic algorithm multi-objective optimization job-shop scheduling
暂未订购 下载PDF
A Review on Swarm Intelligence and Evolutionary Algorithms for Solving Flexible Job Shop Scheduling Problems 认领 引用 被引量：61
11
作者 Kaizhou Gao Zhiguang Cao +3 位作者 Le Zhang Zhenghua Chen Yuyan Han Quanke Pan 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2019年第4期904-916,共13页
Flexible job shop scheduling problems(FJSP)have received much attention from academia and industry for many years.Due to their exponential complexity,swarm intelligence(SI)and evolutionary algorithms(EA)are developed,... Flexible job shop scheduling problems(FJSP)have received much attention from academia and industry for many years.Due to their exponential complexity,swarm intelligence(SI)and evolutionary algorithms(EA)are developed,employed and improved for solving them.More than 60%of the publications are related to SI and EA.This paper intents to give a comprehensive literature review of SI and EA for solving FJSP.First,the mathematical model of FJSP is presented and the constraints in applications are summarized.Then,the encoding and decoding strategies for connecting the problem and algorithms are reviewed.The strategies for initializing algorithms?population and local search operators for improving convergence performance are summarized.Next,one classical hybrid genetic algorithm(GA)and one newest imperialist competitive algorithm(ICA)with variables neighborhood search(VNS)for solving FJSP are presented.Finally,we summarize,discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions. 展开更多
关键词 Evolutionary algorithm flexible job shop scheduling review swarm intelligence
暂未订购 下载PDF
Clonal Selection Based Memetic Algorithm for Job Shop Scheduling Problems 认领 引用 被引量：4
12
作者 Jin-hui Yang Liang Sun +2 位作者 Heow Pueh Lee Yun Qian Yan-chun Liang 《Journal of Bionic Engineering》 SCIE EI 2008年第2期111-119,共9页
A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exp... A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exploration and exploitation. In the clonal selection mechanism, clonal selection, hypermutation and receptor edit theories are presented to construct an evolutionary searching mechanism which is used for exploration. In the local search mechanism, a simulated annealing local search algorithm based on Nowicki and Smutnicki＇s neighborhood is presented to exploit local optima. The proposed algorithm is examined using some well-known benchmark problems. Numerical results validate the effectiveness of the proposed algorithm. 展开更多
关键词 job shop scheduling problem clonal selection algorithm simulated annealing global search local search
暂未订购 下载PDF
Infeasibility test algorithm and fast repair algorithm of job shop scheduling problem 认领 引用 被引量：1
13
作者 孙璐 黄志 +1 位作者 张惠民 顾文钧 《Journal of Southeast University(English Edition)》 EI CAS 2011年第1期88-91,共4页
To diagnose the feasibility of the solution of a job-shop scheduling problem（JSSP）,a test algorithm based on diagraph and heuristic search is developed and verified through a case study.Meanwhile,a new repair algori... To diagnose the feasibility of the solution of a job-shop scheduling problem（JSSP）,a test algorithm based on diagraph and heuristic search is developed and verified through a case study.Meanwhile,a new repair algorithm for modifying an infeasible solution of the JSSP to become a feasible solution is proposed for the general JSSP.The computational complexity of the test algorithm and the repair algorithm is both O（n） under the worst-case scenario,and O（2J＋M） for the repair algorithm under the best-case scenario.The repair algorithm is not limited to specific optimization methods,such as local tabu search,genetic algorithms and shifting bottleneck procedures for job shop scheduling,but applicable to generic infeasible solutions for the JSSP to achieve feasibility. 展开更多
关键词 infeasibility job shop scheduling repairing algorithm
暂未订购 下载PDF
Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm 认领 引用 被引量：4
14
作者 顾文斌 唐敦兵 郑堃 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2014年第5期559-567,共9页
An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal ... An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms. 展开更多
关键词 job-shop scheduling problem(JSP) hormone modulation mechanism improved adaptive particle swarm optimization(IAPSO) algorithm minimum makespan
暂未订购 下载PDF
Job shop scheduling problem with alternative machines using genetic algorithms 认领 引用 被引量：10
15
作者 I.A.Chaudhry 《Journal of Central South University》 SCIE EI CAS 2012年第5期1322-1333,共12页
The classical job shop scheduling problem(JSP) is the most popular machine scheduling model in practice and is known as NP-hard.The formulation of the JSP is based on the assumption that for each part type or job ther... The classical job shop scheduling problem(JSP) is the most popular machine scheduling model in practice and is known as NP-hard.The formulation of the JSP is based on the assumption that for each part type or job there is only one process plan that prescribes the sequence of operations and the machine on which each operation has to be performed.However,JSP with alternative machines for various operations is an extension of the classical JSP,which allows an operation to be processed by any machine from a given set of machines.Since this problem requires an additional decision of machine allocation during scheduling,it is much more complex than JSP.We present a domain independent genetic algorithm(GA) approach for the job shop scheduling problem with alternative machines.The GA is implemented in a spreadsheet environment.The performance of the proposed GA is analyzed by comparing with various problem instances taken from the literatures.The result shows that the proposed GA is competitive with the existing approaches.A simplified approach that would be beneficial to both practitioners and researchers is presented for solving scheduling problems with alternative machines. 展开更多
关键词 alternative machine genetic algorithm （GA） job shop, scheduling spreadsheet
暂未订购 下载PDF
Approximation algorithm for multiprocessor parallel job scheduling 认领 引用 被引量：1
16
作者 陈松乔 黄金贵 陈建二 《Journal of Central South University of Technology》 2002年第4期267-272,共6页
P k |fix| C max problem is a new scheduling problem based on the multiprocessor parallel job, and it is proved to be NP hard problem when k ≥3. This paper focuses on the case of k =3. Some new observations and new te... P k |fix| C max problem is a new scheduling problem based on the multiprocessor parallel job, and it is proved to be NP hard problem when k ≥3. This paper focuses on the case of k =3. Some new observations and new techniques for P 3 |fix| C max problem are offered. The concept of semi normal schedulings is introduced, and a very simple linear time algorithm Semi normal Algorithm for constructing semi normal schedulings is developed. With the method of the classical Graham List Scheduling, a thorough analysis of the optimal scheduling on a special instance is provided, which shows that the algorithm is an approximation algorithm of ratio of 9/8 for any instance of P 3|fix| C max problem, and improves the previous best ratio of 7/6 by M.X.Goemans. 展开更多
关键词 multiprocessor parallel job scheduling approximation algorithm NP-hard problem
暂未订购 下载PDF
Job-shop Scheduling with Multi-objectives Based on Genetic Algorithms 认领 引用 被引量：1
17
作者 周亚勤 李蓓智 陈革 《Journal of Donghua University(English Edition)》 EI CAS 2003年第3期57-62,共6页
The technology of production planning and scheduling is one of the critical technologies that decide whether the automated manufacturing systems can get the expected economy. Job shop scheduling belongs to the special... The technology of production planning and scheduling is one of the critical technologies that decide whether the automated manufacturing systems can get the expected economy. Job shop scheduling belongs to the special class of NP-hard problems. Most of the algorithms used to optimize this class of problems have an exponential time; that is, the computation time increases exponentially with problem size. In scheduling study, makespan is often considered as the main objective. In this paper, makespan, the due date request of the key jobs, the availability of the key machine, the average wait-time of the jobs, and the similarities between the jobs and so on are taken into account based on the application of mechanical engineering. The job shop scheduling problem with multi-objectives is analyzed and studied by using genetic algorithms based on the mechanics of genetics and natural selection. In this research, the tactics of the coding and decoding and the design of the genetic operators, along with the description of the mathematic model of the multi-objective functions, are presented. Finally an illu-strative example is given to testify the validity of this algorithm. 展开更多
关键词 job shop scheduling multi-objective optimization genetic algorithms
暂未订购 下载PDF
An Improved Genetic Algorithm for Solving the Mixed⁃Flow Job⁃Shop Scheduling Problem with Combined Processing Constraints 认领 引用 被引量：4
18
作者 ZHU Haihua ZHANG Yi +2 位作者 SUN Hongwei LIAO Liangchuang TANG Dunbing 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2021年第3期415-426,共12页
The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied.... The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied.Targeting this problem,the process state model of a mixed-flow production line is analyzed.On this basis,a mathematical model of a mixed-flow job-shop scheduling problem with combined processing constraints is established based on the traditional FJSP.Then,an improved genetic algorithm with multi-segment encoding,crossover,and mutation is proposed for the mixed-flow production line problem.Finally,the proposed algorithm is applied to the production workshop of missile structural components at an aerospace institute to verify its feasibility and effectiveness. 展开更多
关键词 mixed-flow production flexible job-shop scheduling problem(FJSP) genetic algorithm encoding
暂未订购 下载PDF
EFFICIENT MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM FOR JOB SHOP SCHEDULING 认领 引用
19
作者 Lei Deming Wu Zhiming 《Chinese Journal of Mechanical Engineering》 EI CAS 2005年第4期494-497,共4页
A new representation method is first presented based on priority roles.According to this method,each entry in the chromosome indicates that in the procedure of the Giffler and Thompson(GT)algorithm,the conflict occurr... A new representation method is first presented based on priority roles.According to this method,each entry in the chromosome indicates that in the procedure of the Giffler and Thompson(GT)algorithm,the conflict occurring in the corresponding machine is resolved by the corresponding priority role.Then crowding-measure multi-objective evolutionary algorithm(CMOEA)is designed,in which both archive maintenance and fitness assignment use crowding measure.Finally the comparisons between CMOEA and SPEA in solving 15 scheduling problems demonstrate that CMOEA is suitable to job shop scheduling. 展开更多
关键词 Job shop Crowding measure Archive maintenance Fitness assignment Multi-objective evolutionary algorithm
暂未订购 下载PDF
A Dynamic Job Scheduling Algorithm for Parallel System 认领 引用
20
作者 张建 陆鑫达 加力 《Journal of Shanghai Jiaotong university(Science)》 2003年第1期10-14,共5页
One of the fundamental problems in parallel and distributed systems is deciding how to allocate jobs to processors. The goals of job scheduling in a parallel environment are to minimize the parallel execution time of ... One of the fundamental problems in parallel and distributed systems is deciding how to allocate jobs to processors. The goals of job scheduling in a parallel environment are to minimize the parallel execution time of a job and try to balance the user’s desire with the system’s desire. The users always want their jobs be completed as quickly as possible, while the system wants to service as many jobs as possible. In this paper, a dynamic job scheduling algorithm was introduced. This algorithm tries to utilize the information of a practical system to allocate the jobs more evenly. The communication time between the processor and scheduler is overlapped with the computation time of the processor. So the communication overhead can be little. The principle of scheduling the job is based on the desirability of each processor. The scheduler would not allocate a new job to a processor that is already fully utilized. The execution efficiency of the system will be increased. This algorithm also can be reused in other complex algorithms. 展开更多
关键词 parallel system job scheduling dynamic scheduling job queue
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 250 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈