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基于批加工的semi-flow-shop生产调度优化 被引量:2
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作者 刘林 刘心报 杨善林 《中国机械工程》 EI CAS CSCD 北大核心 2009年第19期2326-2331,共6页
提出了一种类似于flow-shop但又区别于flow-shop的semi-flow-shop生产调度问题,即根据各自的工艺要求,在同一生产线上以批为单位加工的工件可以跳过生产线上的一些工序,直接进入下道工序。根据实际需求,其调度目标不仅要考虑产品的提前... 提出了一种类似于flow-shop但又区别于flow-shop的semi-flow-shop生产调度问题,即根据各自的工艺要求,在同一生产线上以批为单位加工的工件可以跳过生产线上的一些工序,直接进入下道工序。根据实际需求,其调度目标不仅要考虑产品的提前/拖期,而且还要考虑设备的空闲。针对该问题,设计了一种改进的遗传算法,基因信息熵的概念被用于共享函数、自适应交叉概率和变异概率的计算,遗传算法的性能得以进一步改善。 展开更多
关键词 生产调度 semi-flow-shop 遗传算法
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考虑工件准备时间的Semi-Flow-Shop装配调度问题研究 被引量:1
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作者 陈慈波 徐克林 申屠锦锦 《现代制造工程》 CSCD 北大核心 2011年第3期88-92,129,共6页
提出一种类似于Flow-Shop调度问题(FSP)但又区别于FSP的Semi-Flow-Shop装配调度问题(SFSP),即在系列产品的装配中,某些产品可以跳过装配工艺中的几道工序而直接进入下道工序。根据实际情况,同时考虑了工件的准备时间及劳动力资源约束。... 提出一种类似于Flow-Shop调度问题(FSP)但又区别于FSP的Semi-Flow-Shop装配调度问题(SFSP),即在系列产品的装配中,某些产品可以跳过装配工艺中的几道工序而直接进入下道工序。根据实际情况,同时考虑了工件的准备时间及劳动力资源约束。针对该类问题,以最小化生产周期为目标建立数学模型,并设计了一种改进的双层自适应单亲遗传算法,实例证明该算法具有较高的有效性。 展开更多
关键词 装配调度 Semi—Flow—shop装配调度 单亲遗传算法
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TikTok Shop在东南亚五国的发展策略研究——基于SWOT-AHP模型的分析
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作者 陈德慧 崔瑞 《北方经贸》 2024年第6期23-26,31,共5页
伴随全球电商渗透率持续提升,我国的一批优秀企业纷纷开通跨境电商平台,以更好地助力品牌出海。字节跳动旗下的全球最大的短视频网站之一Tiktok就是其中的典型代表,其在东南亚五国开通了Titok Shop,为我国企业开拓东南亚市场搭建优质的... 伴随全球电商渗透率持续提升,我国的一批优秀企业纷纷开通跨境电商平台,以更好地助力品牌出海。字节跳动旗下的全球最大的短视频网站之一Tiktok就是其中的典型代表,其在东南亚五国开通了Titok Shop,为我国企业开拓东南亚市场搭建优质的跨境电商平台。首先总结了TikTok Shop在东南亚五国开展业务的现状,采用SWO T-AHP方法对TikTok Shop在东南亚五国的发展进行深入剖析,认为其战略发展方向应以SO战略为主;提出TikTok Shop应完善跨境电商物流体系、深耕本土化运营、完善品牌化建设、加强基础设施建设、培养跨境电商复合型人才等发展策略,以期为我国企业搭建优质的跨境电商平台,助力我国品牌出海提供有价值的参考。 展开更多
关键词 跨境电商 SWOT-AHP分析 东南亚 TikTok shop
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Q-Learning-Assisted Meta-Heuristics for Scheduling Distributed Hybrid Flow Shop Problems
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作者 Qianyao Zhu Kaizhou Gao +2 位作者 Wuze Huang Zhenfang Ma Adam Slowik 《Computers, Materials & Continua》 SCIE EI 2024年第9期3573-3589,共17页
The flow shop scheduling problem is important for the manufacturing industry.Effective flow shop scheduling can bring great benefits to the industry.However,there are few types of research on Distributed Hybrid Flow S... The flow shop scheduling problem is important for the manufacturing industry.Effective flow shop scheduling can bring great benefits to the industry.However,there are few types of research on Distributed Hybrid Flow Shop Problems(DHFSP)by learning assisted meta-heuristics.This work addresses a DHFSP with minimizing the maximum completion time(Makespan).First,a mathematical model is developed for the concerned DHFSP.Second,four Q-learning-assisted meta-heuristics,e.g.,genetic algorithm(GA),artificial bee colony algorithm(ABC),particle swarm optimization(PSO),and differential evolution(DE),are proposed.According to the nature of DHFSP,six local search operations are designed for finding high-quality solutions in local space.Instead of randomselection,Q-learning assists meta-heuristics in choosing the appropriate local search operations during iterations.Finally,based on 60 cases,comprehensive numerical experiments are conducted to assess the effectiveness of the proposed algorithms.The experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random strategy.To verify the competitiveness of the Q-learning assistedmeta-heuristics,they are compared with the improved iterated greedy algorithm(IIG),which is also for solving DHFSP.The Friedman test is executed on the results by five algorithms.It is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG,and the Q-learning-assisted PSO shows the best competitiveness. 展开更多
关键词 Distributed scheduling hybrid flow shop META-HEURISTICS local search Q-LEARNING
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An Elite-Class Teaching-Learning-Based Optimization for Reentrant Hybrid Flow Shop Scheduling with Bottleneck Stage
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作者 Deming Lei Surui Duan +1 位作者 Mingbo Li Jing Wang 《Computers, Materials & Continua》 SCIE EI 2024年第4期47-63,共17页
Bottleneck stage and reentrance often exist in real-life manufacturing processes;however,the previous research rarely addresses these two processing conditions in a scheduling problem.In this study,a reentrant hybrid ... Bottleneck stage and reentrance often exist in real-life manufacturing processes;however,the previous research rarely addresses these two processing conditions in a scheduling problem.In this study,a reentrant hybrid flow shop scheduling problem(RHFSP)with a bottleneck stage is considered,and an elite-class teaching-learning-based optimization(ETLBO)algorithm is proposed to minimize maximum completion time.To produce high-quality solutions,teachers are divided into formal ones and substitute ones,and multiple classes are formed.The teacher phase is composed of teacher competition and teacher teaching.The learner phase is replaced with a reinforcement search of the elite class.Adaptive adjustment on teachers and classes is established based on class quality,which is determined by the number of elite solutions in class.Numerous experimental results demonstrate the effectiveness of new strategies,and ETLBO has a significant advantage in solving the considered RHFSP. 展开更多
关键词 Hybrid flow shop scheduling REENTRANT bottleneck stage teaching-learning-based optimization
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China's E-commerce Operators Ready for Double 11 Shopping Festival
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作者 Ada Wang 《China's Foreign Trade》 2024年第5期39-40,共2页
With one month until November 11,all major e-commerce platforms have started their preparations for this great event.The"Double 11"shopping festival was first initiated by Alibaba in 2009and is the world'... With one month until November 11,all major e-commerce platforms have started their preparations for this great event.The"Double 11"shopping festival was first initiated by Alibaba in 2009and is the world's largest online sales gala.This shopping festival is thus named due to the date of November11 and has extended from the original24 hours to several weeks in recent years.The pre-sales stage start from late October.Some new e-commerce companies like TikTok and Pinduoduo have also been involved in the event. 展开更多
关键词 COMPANIES shopPING COMMERCE
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Correlation between psychological resilience and burnout among female employees in a shopping mall in Xi Xian new area,China:A cross-sectional survey-Retraction
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《Journal of Integrative Nursing》 2024年第1期70-70,共1页
In the article titled“Correlation between psychological resilience and burnout among female employees in a shopping mall in Xi Xian New Area,China:A cross-sectional survey”by Zhang Q and Liu L(J Integr Nurs 2021;3(3... In the article titled“Correlation between psychological resilience and burnout among female employees in a shopping mall in Xi Xian New Area,China:A cross-sectional survey”by Zhang Q and Liu L(J Integr Nurs 2021;3(3):117-121.doi:10.4103/jin.jin_14_21),[1]the content and results data of this article was questioned by International database(Web of Science)institution.This article was then investigated by the publisher and Journal of Integrative Nursing(JIN). 展开更多
关键词 shopPING database sectional
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Strengthened Dominance Relation NSGA-Ⅲ Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem
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作者 Liang Zeng Junyang Shi +2 位作者 Yanyan Li Shanshan Wang Weigang Li 《Computers, Materials & Continua》 SCIE EI 2024年第1期375-392,共18页
The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various ... The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem. 展开更多
关键词 Multi-objective job shop scheduling non-dominated sorting genetic algorithm differential evolution simulated binary crossover
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Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer
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作者 Hongliang Zhang Yi Chen +1 位作者 Yuteng Zhang Gongjie Xu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第8期1459-1483,共25页
The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worke... The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality. 展开更多
关键词 Distributed flexible job shop scheduling problem dual resource constraints energy-saving scheduling multi-objective grey wolf optimizer Q-LEARNING
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The influence of different types of satisfaction on loyalty on C2C online shopping platform:From the perspective of sellers and the platform
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作者 Yanan Lu Qian Huang Yuting Wang 《中国科学技术大学学报》 CAS CSCD 北大核心 2024年第5期36-48,I0007,共14页
With the rise and development of major types of platforms,the competition for resources has become extremely fierce,and the market share of C2C platforms has been seriously threatened by the loss of resources.Therefor... With the rise and development of major types of platforms,the competition for resources has become extremely fierce,and the market share of C2C platforms has been seriously threatened by the loss of resources.Therefore,building and maintaining buyers’satisfaction and loyalty to C2C platforms is critical to the survival and sustainability of C2C platforms in China.However,the current knowledge on how platform satisfaction and loyalty are constructed in the C2C e-commerce environment is incomplete.In this study,seller-based satisfaction and platform-based satisfaction are constructed separately.We further distinguish seller-based transaction satisfaction into economic and social satisfaction and explore their antecedents and consequences.To test our research hypotheses,we conduct a survey and collect data from a real online market(Taobao website).The results show that seller-based transaction satisfaction positively affects platform-based overall satisfaction and loyalty,and that perceived product quality,perceived assurance,and perceived price fairness all have a significant effect on economic satisfaction,whereas perceived relationship quality and perceived empathy significantly influence social satisfaction.These findings help us understand the literature related to customer satisfaction in the context of C2C in China and provide inspiration for online sellers and platforms. 展开更多
关键词 transaction-specific satisfaction social and economic satisfaction various antecedents of satisfaction overall satisfaction C2C online shopping platform
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An Improved Harris Hawk Optimization Algorithm for Flexible Job Shop Scheduling Problem
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作者 Zhaolin Lv Yuexia Zhao +2 位作者 Hongyue Kang Zhenyu Gao Yuhang Qin 《Computers, Materials & Continua》 SCIE EI 2024年第2期2337-2360,共24页
Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been... Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been widely employed to solve scheduling problems.However,HHO suffers from premature convergence when solving NP-hard problems.Therefore,this paper proposes an improved HHO algorithm(GNHHO)to solve the FJSP.GNHHO introduces an elitism strategy,a chaotic mechanism,a nonlinear escaping energy update strategy,and a Gaussian random walk strategy to prevent premature convergence.A flexible job shop scheduling model is constructed,and the static and dynamic FJSP is investigated to minimize the makespan.This paper chooses a two-segment encoding mode based on the job and the machine of the FJSP.To verify the effectiveness of GNHHO,this study tests it in 23 benchmark functions,10 standard job shop scheduling problems(JSPs),and 5 standard FJSPs.Besides,this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s FJSP.The optimized scheduling scheme demonstrates significant improvements in makespan,with an advancement of 28.16%for static scheduling and 35.63%for dynamic scheduling.Moreover,it achieves an average increase of 21.50%in the on-time order delivery rate.The results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms. 展开更多
关键词 Flexible job shop scheduling improved Harris hawk optimization algorithm(GNHHO) premature convergence maximum completion time(makespan)
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Deep Reinforcement Learning Solves Job-shop Scheduling Problems
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作者 Anjiang Cai Yangfan Yu Manman Zhao 《Instrumentation》 2024年第1期88-100,共13页
To solve the sparse reward problem of job-shop scheduling by deep reinforcement learning,a deep reinforcement learning framework considering sparse reward problem is proposed.The job shop scheduling problem is transfo... To solve the sparse reward problem of job-shop scheduling by deep reinforcement learning,a deep reinforcement learning framework considering sparse reward problem is proposed.The job shop scheduling problem is transformed into Markov decision process,and six state features are designed to improve the state feature representation by using two-way scheduling method,including four state features that distinguish the optimal action and two state features that are related to the learning goal.An extended variant of graph isomorphic network GIN++is used to encode disjunction graphs to improve the performance and generalization ability of the model.Through iterative greedy algorithm,random strategy is generated as the initial strategy,and the action with the maximum information gain is selected to expand it to optimize the exploration ability of Actor-Critic algorithm.Through validation of the trained policy model on multiple public test data sets and comparison with other advanced DRL methods and scheduling rules,the proposed method reduces the minimum average gap by 3.49%,5.31%and 4.16%,respectively,compared with the priority rule-based method,and 5.34%compared with the learning-based method.11.97%and 5.02%,effectively improving the accuracy of DRL to solve the approximate solution of JSSP minimum completion time. 展开更多
关键词 job shop scheduling problems deep reinforcement learning state characteristics policy network
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Analyzing the Application of Traditional Chinese Cultural Elements in Brand Packaging Design With Design Semiotics:A Case Study of Modern China Tea Shop
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作者 Ziyang Huang Euitai Jung 《Psychology Research》 2024年第7期215-222,共8页
This paper takes the Chinese-themed packaging of Modern China Tea Shop as the research object,analyzes the brand positioning and the embodiment of traditional Chinese cultural elements in its brand packaging design,an... This paper takes the Chinese-themed packaging of Modern China Tea Shop as the research object,analyzes the brand positioning and the embodiment of traditional Chinese cultural elements in its brand packaging design,and mainly analyzes the characteristics of traditional Chinese culture and symbols such as painting,text,and color in the packaging design.This paper explores the creative design and application of packaging with traditional Chinese elements in its brand touch points through the analysis method in culture code brand design and points out that the packaging design of Modern China Tea Shop is close to consumer psychology,and the Era Z has gradually become the main force of Chinese consumption.The brand accurately grasps the consumer psychology in the era of Gen-Z so as to formulate corresponding marketing strategies.Combined with the analysis of brand trends and consumers,it is clear that the packaging design of Chinese style is not a simple superposition of traditional elements and modern elements,but the integration and innovation of various cultural elements based on the current market and consumers.Furthermore,the paper summarizes the ways of traditional Chinese elements to create commercial value and provides a feasible reference for the brand positioning and packaging design of other tea products in China. 展开更多
关键词 Modern China Tea shop SEMIOTICS traditional Chinese cultural elements brand packaging design
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考虑动态预维护与绿色调度的协同优化问题
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作者 江雨燕 马宁 +2 位作者 李艳 甘如美江 王付宇 《系统仿真学报》 北大核心 2025年第2期362-378,共17页
针对传统柔性作业车间调度问题,将机器动态预维护与绿色调度进行联合优化,以最小化最大完工时间、总碳排放量、总成本为优化目标建立集成优化模型。提出了一种改进的NSGA-I算法用于求解该模型,采用基于工序、机器和预维护的三层编码方式... 针对传统柔性作业车间调度问题,将机器动态预维护与绿色调度进行联合优化,以最小化最大完工时间、总碳排放量、总成本为优化目标建立集成优化模型。提出了一种改进的NSGA-I算法用于求解该模型,采用基于工序、机器和预维护的三层编码方式,设计了考虑工序分配、机器选择以及机器预维护策略的同步解码方案;改进了精英保留策略,设计了随着代数变化的自适应交又变异函数以及基于邻域搜索的变异算子。实验验证了改进算法在求解不同规模调度问题的有效性,所提的动态预维护策略较其他维护策略能更有效地求解预维护与柔性作业车间绿色调度协同优化问题。 展开更多
关键词 预维护 绿色调度 INSGA-II 协同优化 柔性作业车间
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考虑工序相关性的动态Job shop调度问题启发式算法 被引量:33
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作者 熊禾根 李建军 +2 位作者 孔建益 杨金堂 蒋国璋 《机械工程学报》 EI CAS CSCD 北大核心 2006年第8期50-55,共6页
提出一类考虑工序相关性的、工件批量到达的动态Job shop调度问题,在对工序相关性进行了定义和数学描述的基础上,进一步建立了动态Job shop调度问题的优化模型。设计了一种组合式调度规则RAN(FCFS,ODD),并提出了基于规则的启发式算法以... 提出一类考虑工序相关性的、工件批量到达的动态Job shop调度问题,在对工序相关性进行了定义和数学描述的基础上,进一步建立了动态Job shop调度问题的优化模型。设计了一种组合式调度规则RAN(FCFS,ODD),并提出了基于规则的启发式算法以及该类动态Job shop调度问题的算例生成方法。为验证算法和比较评估调度规则的性能,对算例采用文献提出的7种调度规则和RAN(FCFS,ODD)进行了仿真调度,对调度结果的分析表明了算法的有效性和RAN(FCFS,ODD)调度规则求解所提出的动态Job Shop调度问题的优越性能。 展开更多
关键词 动态Job shop调度 工序相关性 启发式算法 调度规则 仿真
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基于ACPM和BFSM的动态Job-Shop调度算法 被引量:37
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作者 谢志强 刘胜辉 乔佩利 《计算机研究与发展》 EI CSCD 北大核心 2003年第7期977-983,共7页
通过对不同时刻开始加工的产品加工树的分解 ,可将产品加工工序分为具有惟一紧前、紧后的相关工序和独立工序 在对这两类工序研究分批综合应用拟关键路径法 (ACPM )和最佳适应调度方法 (BFSM)调度时 ,考虑了关键设备的工序紧凑性 通过... 通过对不同时刻开始加工的产品加工树的分解 ,可将产品加工工序分为具有惟一紧前、紧后的相关工序和独立工序 在对这两类工序研究分批综合应用拟关键路径法 (ACPM )和最佳适应调度方法 (BFSM)调度时 ,考虑了关键设备的工序紧凑性 通过分析与实例验证 ,所提出的调度方法对解决动态的Job 展开更多
关键词 动态Job—shop调度 拟关键路径法 最佳适应调度法 紧凑
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免疫模拟退火算法及其在柔性动态Job Shop中的应用 被引量:15
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作者 余建军 孙树栋 +1 位作者 王军强 杜先进 《中国机械工程》 EI CAS CSCD 北大核心 2007年第7期793-799,共7页
针对车间作业调度问题,在深入分析免疫算法和模拟退火算法的基础上,将两种算法巧妙结合,提出免疫模拟退火算法。该算法引入了免疫记忆、抽取疫苗和接种疫苗等免疫机制,有助于优良个体和基因的保留和利用,提高了算法收敛性,而且其基于概... 针对车间作业调度问题,在深入分析免疫算法和模拟退火算法的基础上,将两种算法巧妙结合,提出免疫模拟退火算法。该算法引入了免疫记忆、抽取疫苗和接种疫苗等免疫机制,有助于优良个体和基因的保留和利用,提高了算法收敛性,而且其基于概率突跳特性的爬山性能可以避免早熟现象。针对西安航空发动机(集团)有限公司的柔性动态Job Shop,分别用模拟退火算法、免疫算法和免疫模拟退火算法进行了仿真和比较,研究结果表明,免疫模拟退火算法比单一算法性能更优,是求解柔性动态Job Shop问题的有效实用算法。 展开更多
关键词 免疫算法 模拟退火算法 免疫模拟退火算法 柔性 JOB shop
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基于遗传算法求解Job Shop调度优化的新方法 被引量:9
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作者 周辉仁 郑丕谔 +1 位作者 安小会 宗蕴 《系统仿真学报》 CAS CSCD 北大核心 2009年第11期3295-3298,3306,共5页
针对Job Shop调度问题,提出了一种遗传算法编码新方法和矩阵解码方法。该方法根据问题的特点,采用一种按工序进行总体排序染色体编码方案,并采用矩阵解码,解码时体现了编码与调度方案一一对应,并且该编码方案有多种交叉操作算子可用,不... 针对Job Shop调度问题,提出了一种遗传算法编码新方法和矩阵解码方法。该方法根据问题的特点,采用一种按工序进行总体排序染色体编码方案,并采用矩阵解码,解码时体现了编码与调度方案一一对应,并且该编码方案有多种交叉操作算子可用,不需要专门设计算子。算例计算结果表明,基于该编码方案的遗传算法是有效的,能适用解决Job Shop调度问题,通过比较,用该编码方案的遗传算法优化Job Shop调度操作简单并且收敛速度快。 展开更多
关键词 JOB shop调度 遗传算法 编码方法 矩阵解码 优化
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求解Job Shop调度问题的改进禁忌搜索算法 被引量:13
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作者 宋晓宇 孟秋宏 曹阳 《系统工程与电子技术》 EI CSCD 北大核心 2008年第1期93-96,共4页
提出一种改进的禁忌搜索算法,解决传统禁忌搜索算法优化效果对运行次数和初始解依赖的不足,提高这类问题的求解质量。根据Job Shop调度问题解的特征,提出基于关键工序的邻域选择方法,采用此邻域选择方法构造禁忌搜索算法,当无邻域时,重... 提出一种改进的禁忌搜索算法,解决传统禁忌搜索算法优化效果对运行次数和初始解依赖的不足,提高这类问题的求解质量。根据Job Shop调度问题解的特征,提出基于关键工序的邻域选择方法,采用此邻域选择方法构造禁忌搜索算法,当无邻域时,重新产生初始解进行禁忌搜索,将传统的禁忌搜索算法从单起始点搜索改进成多起始点搜索。采用改进的禁忌搜索算法对13个难的benchmarks问题进行10次求解,得到的平均值8个优于TSAB算法,得到的最优解6个优于TSAB算法、4个与TSAB算法相同。采用基于关键工序的邻域结构构造的改进TS算法具有较强的搜索能力。 展开更多
关键词 禁忌搜索算法 JOB shop调度 Giffler&Thompson算法
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解决JOB SHOP问题的粒子群优化算法 被引量:10
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作者 潘全科 王文宏 +1 位作者 潘群 朱剑英 《机械科学与技术》 CSCD 北大核心 2006年第6期675-679,共5页
设计了2种解决Job shop问题的粒子群算法,即实数编码的粒子群调度算法和工序编码的粒子群调度算法。工序编码的粒子群调度算法更符合Job shop问题的特点,优化性能相对高。但粒子群调度算法容易陷入局部最优。为了提高优化性能,将粒子群... 设计了2种解决Job shop问题的粒子群算法,即实数编码的粒子群调度算法和工序编码的粒子群调度算法。工序编码的粒子群调度算法更符合Job shop问题的特点,优化性能相对高。但粒子群调度算法容易陷入局部最优。为了提高优化性能,将粒子群算法和模拟退火算法结合,得到了粒子群-模拟退火混合调度算法。仿真结果表明了算法的有效性。 展开更多
关键词 JOB shop 调度问题 粒子群优化 模拟退火算法
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