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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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Distributed Flexible Job-Shop Scheduling Problem Based on Hybrid Chemical Reaction Optimization Algorithm 被引量:4
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作者 Jialei Li Xingsheng Gu +1 位作者 Yaya Zhang Xin Zhou 《Complex System Modeling and Simulation》 2022年第2期156-173,共18页
Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode.The distributed flexible job-shop scheduling problem(DFJSP)has bec... Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode.The distributed flexible job-shop scheduling problem(DFJSP)has become a research hot topic in the field of scheduling because its production is closer to reality.The research of DFJSP is of great significance to the organization and management of actual production process.To solve the heterogeneous DFJSP with minimal completion time,a hybrid chemical reaction optimization(HCRO)algorithm is proposed in this paper.Firstly,a novel encoding-decoding method for flexible manufacturing unit(FMU)is designed.Secondly,half of initial populations are generated by scheduling rule.Combined with the new solution acceptance method of simulated annealing(SA)algorithm,an improved method of critical-FMU is designed to improve the global and local search ability of the algorithm.Finally,the elitist selection strategy and the orthogonal experimental method are introduced to the algorithm to improve the convergence speed and optimize the algorithm parameters.In the experimental part,the effectiveness of the simulated annealing algorithm and the critical-FMU refinement methods is firstly verified.Secondly,in the comparison with other existing algorithms,the proposed optimal scheduling algorithm is not only effective in homogeneous FMUs examples,but also superior to existing algorithms in heterogeneous FMUs arithmetic cases. 展开更多
关键词 scheduling problem distributed flexible job-shop chemical reaction optimization algorithm heterogeneous factory simulated annealing algorithm
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FLEXIBLE JOB-SHOP SCHEDULING WITH FUZZY GOAL THROUGH IOCDGA 被引量:1
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作者 袁坤 朱剑英 孙志峻 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2006年第2期144-148,共5页
The fuzzy goal flexible job-shop scheduling problem (FGFJSP) is the extension of FJSP. Compared with the convention JSP, it can solve the fuzzy goal problem and meet suit requirements of the key job. The multi-objec... The fuzzy goal flexible job-shop scheduling problem (FGFJSP) is the extension of FJSP. Compared with the convention JSP, it can solve the fuzzy goal problem and meet suit requirements of the key job. The multi-object problem, such as the fuzzy cost, the fuzzy due-date, and the fuzzy makespan, etc, can be solved by FGFJSP. To optimize FGFJSP, an individual optimization and colony diversity genetic algorithm (IOCDGA) is presented to accelerate the convergence speed and to avoid the earliness. In IOCDGA, the colony average distance and the colony entropy are defined after the definition of the encoding model. The colony diversity is expressed by the colony average distance and the colony entropy. The crossover probability and the mutation probability are controlled by the colony diversity. The evolution emphasizes that sigle individual or a few individuals evolve into the best in IOCDGA, but not the all in classical GA. Computational results show that the algorithm is applicable and the number of iterations is less. 展开更多
关键词 genetic algorithm flexible job-shop scheduling fuzzy goal
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INTEGRATED OPERATOR GENETIC ALGORITHM FOR SOLVING MULTI-OBJECTIVE FLEXIBLE JOB-SHOP SCHEDULING
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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
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A Modi ed Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem 被引量:7
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作者 Ghiath Al Aqel Xinyu Li Liang Gao 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2019年第2期157-167,共11页
The flexible job shop scheduling problem(FJSP) is considered as an important problem in the modern manufacturing system. It is known to be an NP-hard problem. Most of the algorithms used in solving FJSP problem are ca... The flexible job shop scheduling problem(FJSP) is considered as an important problem in the modern manufacturing system. It is known to be an NP-hard problem. Most of the algorithms used in solving FJSP problem are categorized as metaheuristic methods. Some of these methods normally consume more CPU time and some other methods are more complicated which make them di cult to code and not easy to reproduce. This paper proposes a modified iterated greedy(IG) algorithm to deal with FJSP problem in order to provide a simpler metaheuristic, which is easier to code and to reproduce than some other much more complex methods. This is done by separating the classical IG into two phases. Each phase is used to solve a sub-problem of the FJSP: sequencing and routing sub-problems. A set of dispatching rules are employed in the proposed algorithm for the sequencing and machine selection in the construction phase of the solution. To evaluate the performance of proposed algorithm, some experiments including some famous FJSP benchmarks have been conducted. By compared with other algorithms, the experimental results show that the presented algorithm is competitive and able to find global optimum for most instances. The simplicity of the proposed IG provides an e ective method that is also easy to apply and consumes less CPU time in solving the FJSP problem. 展开更多
关键词 ITERATED GREEDY flexible JOB SHOP scheduling problem DISPATCHING RULES
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Solving flexible job shop scheduling problem by a multi-swarm collaborative genetic algorithm 被引量:8
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作者 WANG Cuiyu LI Yang LI Xinyu 《Journal of Systems Engineering and Electronics》 SCIE EI 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
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Simultaneous scheduling of machines and automated guided vehicles in flexible manufacturing systems using genetic algorithms 被引量:5
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作者 I.A.Chaudhry S.Mahmood M.Shami 《Journal of Central South University》 SCIE EI CAS 2011年第5期1473-1486,共14页
The problem of simultaneous scheduling of machines and vehicles in flexible manufacturing system (FMS) was addressed.A spreadsheet based genetic algorithm (GA) approach was presented to solve the problem.A domain inde... The problem of simultaneous scheduling of machines and vehicles in flexible manufacturing system (FMS) was addressed.A spreadsheet based genetic algorithm (GA) approach was presented to solve the problem.A domain independent general purpose GA was used,which was an add-in to the spreadsheet software.An adaptation of the propritary GA software was demonstrated to the problem of minimizing the total completion time or makespan for simultaneous scheduling of machines and vehicles in flexible manufacturing systems.Computational results are presented for a benchmark with 82 test problems,which have been constructed by other researchers.The achieved results are comparable to the previous approaches.The proposed approach can be also applied to other problems or objective functions without changing the GA routine or the spreadsheet model. 展开更多
关键词 automated guided vehicles (AGVs) scheduling job-shop genetic algorithms flexible manufacturing system (FMS) SPREADSHEET
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APPLYING PARTICLE SWARM OPTIMIZATION TO JOB-SHOPSCHEDULING PROBLEM 被引量:5
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作者 XiaWeijun WuZhiming ZhangWei YangGenke 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2004年第3期437-441,共5页
A new heuristic algorithm is proposed for the problem of finding the minimummakespan in the job-shop scheduling problem. The new algorithm is based on the principles ofparticle swarm optimization (PSO). PSO employs a ... A new heuristic algorithm is proposed for the problem of finding the minimummakespan in the job-shop scheduling problem. The new algorithm is based on the principles ofparticle swarm optimization (PSO). PSO employs a collaborative population-based search, which isinspired by the social behavior of bird flocking. It combines local search (by self experience) andglobal search (by neighboring experience), possessing high search efficiency. Simulated annealing(SA) employs certain probability to avoid becoming trapped in a local optimum and the search processcan be controlled by the cooling schedule. By reasonably combining these two different searchalgorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, isdeveloped. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated byapplying it to some benchmark job-shop scheduling problems and comparing results with otheralgorithms in literature. Comparing results indicate that PSO-based algorithm is a viable andeffective approach for the job-shop scheduling problem. 展开更多
关键词 job-shop scheduling problem Particle swarm optimization Simulated annealingHybrid optimization algorithm
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Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm 被引量:3
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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
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A Grafted Genetic Algorithm for the Job-Shop Scheduling Problem 被引量:1
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作者 LIXiang-jun WANGShu-zhen XUGuo-hua 《International Journal of Plant Engineering and Management》 2004年第2期91-96,共6页
The standard genetic algorithm has limitations of a low convergence rate and premature convergence in solving the job-shop scheduling problem.To overcome these limitations,this paper presents a new improved hybrid gen... The standard genetic algorithm has limitations of a low convergence rate and premature convergence in solving the job-shop scheduling problem.To overcome these limitations,this paper presents a new improved hybrid genetic algorithm on the basis of the idea of graft in botany.Through the introduction of a grafted population and crossover probability matrix,this algorithm accelerates the convergence rate greatly and also increases the ability to fight premature convergence.Finally,the approach is tested on a set of standard instances taken from the literature and compared with other approaches.The computation results validate the effectiveness of the proposed algorithm. 展开更多
关键词 grafted genetic algorithm job-shop scheduling problem premature convergence hy brid optimization strategy
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An Improved Genetic Algorithm for Solving the Mixed⁃Flow Job⁃Shop Scheduling Problem with Combined Processing Constraints 被引量:4
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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
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Hybrid heuristic algorithm for multi-objective scheduling problem 被引量:3
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作者 PENG Jian'gang LIU Mingzhou +1 位作者 ZHANG Xi LING Lin 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2019年第2期327-342,共16页
This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-object... This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems(MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning(OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory(HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution(TOPSIS), are implemented for solving the MOFJSP.Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies.Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP. 展开更多
关键词 flexible job-shop scheduling HARMONY SEARCH (HS) algorithm PARETO OPTIMALITY opposition-based learning
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Domain Knowledge Used in Meta-Heuristic Algorithms for the Job-Shop Scheduling Problem:Review and Analysis
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作者 Lin Gui Xinyu Li +1 位作者 Qingfu Zhang Liang Gao 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2024年第5期1368-1389,共22页
Meta-heuristic algorithms search the problem solution space to obtain a satisfactory solution within a reasonable timeframe.By combining domain knowledge of the specific optimization problem,the search efficiency and ... Meta-heuristic algorithms search the problem solution space to obtain a satisfactory solution within a reasonable timeframe.By combining domain knowledge of the specific optimization problem,the search efficiency and quality of meta-heuristic algorithms can be significantly improved,making it crucial to identify and summarize domain knowledge within the problem.In this paper,we summarize and analyze domain knowledge that can be applied to meta-heuristic algorithms in the job-shop scheduling problem(JSP).Firstly,this paper delves into the importance of domain knowledge in optimization algorithm design.After that,the development of different methods for the JSP are reviewed,and the domain knowledge in it for meta-heuristic algorithms is summarized and classified.Applications of this domain knowledge are analyzed,showing it is indispensable in ensuring the optimization performance of meta-heuristic algorithms.Finally,this paper analyzes the relationship among domain knowledge,optimization problems,and optimization algorithms,and points out the shortcomings of the existing research and puts forward research prospects.This paper comprehensively summarizes the domain knowledge in the JSP,and discusses the relationship between the optimization problems,optimization algorithms and domain knowledge,which provides a research direction for the metaheuristic algorithm design for solving the JSP in the future. 展开更多
关键词 domain knowledge job-shop scheduling problem meta-heuristic algorithm
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A hybrid genetic algorithm for multi-objective flexible job shop scheduling problem considering transportation time 被引量:8
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作者 Xiabao Huang Lixi Yang 《International Journal of Intelligent Computing and Cybernetics》 EI 2019年第2期154-174,共21页
Purpose–Flexible job-shop scheduling is significant for different manufacturing industries nowadays.Moreover,consideration of transportation time during scheduling makes it more practical and useful.The purpose of th... Purpose–Flexible job-shop scheduling is significant for different manufacturing industries nowadays.Moreover,consideration of transportation time during scheduling makes it more practical and useful.The purpose of this paper is to investigate multi-objective flexible job-shop scheduling problem(MOFJSP)considering transportation time.Design/methodology/approach–A hybrid genetic algorithm(GA)approach is integrated with simulated annealing to solve the MOFJSP considering transportation time,and an external elitism memory library is employed as a knowledge library to direct GA search into the region of better performance.Findings–The performance of the proposed algorithm is tested on different MOFJSP taken from literature.Experimental results show that proposed algorithm performs better than the original GA in terms of quality of solution and distribution of the solution,especially when the number of jobs and the flexibility of the machine increase.Originality/value–Most of existing studies have not considered the transportation time during scheduling of jobs.The transportation time is significantly desired to be included in the FJSP when the time of transportation of jobs has significant impact on the completion time of jobs.Meanwhile,GA is one of primary algorithms extensively used to address MOFJSP in literature.However,to solve the MOFJSP,the original GA has a possibility to get a premature convergence and it has a slow convergence speed.To overcome these problems,a new hybrid GA is developed in this paper. 展开更多
关键词 flexible job-shop scheduling problem Transportation time Genetic algorithm Simulated annealing Multi-objective optimization
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考虑模糊质检时间的柔性作业车间动态调度问题
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作者 张晓楠 龚嘉龙 +2 位作者 姜帅 王陆宇 李阳 《计算机应用研究》 CSCD 北大核心 2024年第8期2351-2359,共9页
为解决更符合现实情形的模糊质检时间柔性作业车间动态调度问题,以最小化完工时间为目标,立足紧急插单、机器在空载运行时发生故障和机器在加工工件时发生故障的三种故障情形,建立了带模糊质检时间的机器故障、紧急插单重调度模型。设... 为解决更符合现实情形的模糊质检时间柔性作业车间动态调度问题,以最小化完工时间为目标,立足紧急插单、机器在空载运行时发生故障和机器在加工工件时发生故障的三种故障情形,建立了带模糊质检时间的机器故障、紧急插单重调度模型。设计了基于元胞自动机邻域搜索和随机重启爬坡算法的改进遗传算法求解模型,即针对车间调度问题中存在的订单排序和机器选择双决策问题特征,设计包含工序码和机器码的双层编码方案,并基于遗传算法思想对工序码和机器码设计相应的交叉、变异等遗传操作。同时,将遗传操作应用于基于元胞自动机的邻域搜索算法框架中以增强算法全局搜索能力,整合基于关键工序的随机重启爬坡算法以提高算法局部开发能力。实验选取10个柔性车间调度算例验证了所提算法的有效性,同时,测试1个模糊质检时间柔性车间调度算例验证了模型的有效性。另外,实验也测试了不同故障场景,得出该动态调度方法优于实际场景中常使用的“工件后移”调度策略。 展开更多
关键词 柔性作业车间调度问题 模糊质检时间 重调度 遗传算法
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协同智能体强化学习算法的柔性作业车间调度方法研究
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作者 李健 李洹坤 +3 位作者 何鹏博 王化北 徐莉萍 何奎 《系统仿真学报》 CAS CSCD 北大核心 2024年第11期2699-2711,共13页
为提高柔性作业车间调度效率,构建一种具有柔性作业车间调度问题约束条件的马尔可夫决策过程,针对工件与机器的同时选择问题,提出一种协同智能体强化学习方法进行求解。在构建马尔可夫决策过程中,引入析取图表述状态特征,采用两种智能... 为提高柔性作业车间调度效率,构建一种具有柔性作业车间调度问题约束条件的马尔可夫决策过程,针对工件与机器的同时选择问题,提出一种协同智能体强化学习方法进行求解。在构建马尔可夫决策过程中,引入析取图表述状态特征,采用两种智能体执行工件与机器的选取,预测不同时刻最小化最大完工时间的差值来映射整个调度过程的奖励参数;求解时,嵌入GIN(graph isomorphic network)图神经网络提取状态,为工件与机器智能体分别设置编码器-解码器构件输出两种动作策略,以PPO(proximal policy optimization)算法与D3QN算法训练工件与机器智能体的决策网络参数。通过正交试验法选取算法超参数,以标准实例与其他文献进行对比,实验结果表明,所提方法在求解FJSP方面明显优于其他算法,进一步验证所提方法的可行性与有效性。 展开更多
关键词 柔性作业车间调度问题 图神经网络 马尔可夫决策过程 协同智能体强化学习 正交试验法
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混合遗传变邻域搜索算法求解柔性车间调度问题
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作者 周伟 孙瑜 +1 位作者 李西兴 王林琳 《计算机工程与设计》 北大核心 2024年第7期2041-2049,共9页
针对考虑生产成本的柔性作业车间调度问题(flow job shop scheduling problem, FJSP),以完工时间与加工成本为优化指标,提出一种求解FJSP的混合遗传变邻域搜索算法。根据个体适应度对种群分割,结合自适应交叉概率改进子代种群产生方式;... 针对考虑生产成本的柔性作业车间调度问题(flow job shop scheduling problem, FJSP),以完工时间与加工成本为优化指标,提出一种求解FJSP的混合遗传变邻域搜索算法。根据个体适应度对种群分割,结合自适应交叉概率改进子代种群产生方式;设计两种邻域结构增强算法的局部搜索能力;提出一种基于动态交叉变异概率的优化算法流程提高求解效率。运用提出的算法求解基准实例与实际问题测试,验证了算法的有效性。 展开更多
关键词 柔性作业车间调度 加工成本 遗传算法 变邻域搜索 混合算法 动态概率 优化
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双种群混合遗传算法求解航空复合材料柔性调度问题
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作者 王玉芳 姚彬彬 +1 位作者 陈凡 曾亚志 《计算机工程与设计》 北大核心 2024年第10期3143-3152,共10页
考虑航空复合材料柔性车间调度中的运输约束,以最小化完工时间为目标,建立调度模型,提出一种改进的双种群混合遗传算法进行求解。根据问题特点,基于工序排序、机器选择和运输约束3个子问题,设计三层实数编码以及对应解码方案。采用混合... 考虑航空复合材料柔性车间调度中的运输约束,以最小化完工时间为目标,建立调度模型,提出一种改进的双种群混合遗传算法进行求解。根据问题特点,基于工序排序、机器选择和运输约束3个子问题,设计三层实数编码以及对应解码方案。采用混合初始化提高种群质量,进化过程中采用交叉算子执行全局搜索,为双种群设计基于机器负载平衡和变邻域的局部搜索,提高全局和局部搜索能力。与对比算法相比10个测试算例中BPRD指标取得9个最优,APRD指标全部取得最优,t检验显著性有明显差异,验证算法的优越性。将算法应用于航空复合材料车间中,实现实际生产的调度,验证算法的可行性。 展开更多
关键词 航空复合材料 柔性作业车间调度 双种群 混合遗传算法 运输约束 机器负载平衡 变邻域
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考虑双资源约束的分布式柔性作业车间调度
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作者 张洪亮 陈毅 《哈尔滨商业大学学报(自然科学版)》 CAS 2024年第5期631-640,共10页
随着全球制造业的发展,分布式柔性作业车间调度问题(distributed flexible job shop scheduling problem, DFJSP)引起了学者们的关注.DFJSP的研究中常常忽略工人资源,作为生产的关键因素,有效利用工人资源可以提高生产率.研究了考虑双... 随着全球制造业的发展,分布式柔性作业车间调度问题(distributed flexible job shop scheduling problem, DFJSP)引起了学者们的关注.DFJSP的研究中常常忽略工人资源,作为生产的关键因素,有效利用工人资源可以提高生产率.研究了考虑双资源约束的分布式柔性作业车间调度问题(distributed flexible job shop scheduling problem with dual resource constraints, DFJSP-DRC),建立以最小化最大完工时间和总能耗为目标的数学模型,并提出一种改进的非支配排序遗传算法(improved non-dominated sorting genetic algorithm, INSGA-Ⅱ)去求解.在INSGA-Ⅱ中,通过混合初始化策略生成高质量的初始解,并设计了一种基于加工机器和工人公共空闲时间的主动解码策略来获得调度方案.为增强INSGA-Ⅱ的全局搜索能力,提出了改进的交叉变异策略和自适应交叉变异率.通过在45个算例与三种算法的比较,验证了INSGA-Ⅱ解决DFJSP-DRC的有效性. 展开更多
关键词 分布式柔性作业车间调度 节能调度 双资源约束 多目标优化 非支配排序遗传算法 主动解码
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基于改进樽海鞘群算法的多目标柔性作业车间调度问题研究
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作者 张洪亮 曹恒婉 《安徽工业大学学报(社会科学版)》 2024年第3期17-23,共7页
针对多目标柔性作业车间调度问题,构建了以最小化总能耗、最小化生产成本及最小化惩罚值为优化目标的数学模型,并设计改进的多目标樽海鞘群算法(IMSSA)进行求解。改进算法主要由樽海鞘领导者和樽海鞘追随者两部分构成,其中,领导者位置... 针对多目标柔性作业车间调度问题,构建了以最小化总能耗、最小化生产成本及最小化惩罚值为优化目标的数学模型,并设计改进的多目标樽海鞘群算法(IMSSA)进行求解。改进算法主要由樽海鞘领导者和樽海鞘追随者两部分构成,其中,领导者位置更新结合正余弦算法来实现,追随者位置更新基于线性微分递减的惯性权重方法来完成。此外,引入食物源存储库用于保留非支配解。最后通过对比实验证明了所提策略及改进算法的有效性。 展开更多
关键词 柔性作业车间调度问题 多目标优化 樽海鞘群算法
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