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1.
研究车间作业调度优化过程,针对资源的合理分配排序,采用PSO算法求解柔性作业车间调度问题,根据PSO算法存在易陷入局部极值和早熟的缺陷,引入遗传算法中的交叉算子和变异算子,构造求解柔性作业车间调度问题的混合PSO算法,能够较好地克服上述缺陷.采用面向对象的程序设计语言,设计并编码实现了混合PSO算法求解柔性作业车间调度问题的仿真软件.使用软件进行仿真,实验结果表明在求解柔性作业车间调度问题中,混合PSO算法的全局寻优和克服早熟能力均优于基本PSO算法,证明混合PSO算法求解柔性作业车间调度问题的有效性.  相似文献   

2.
应用Agent理论的生产调度系统研究   总被引:1,自引:0,他引:1  
生产调度问题,一般可根据生产流程的不同分为Job-shop调度和Flowshop调度两大类(也有学者认为,存在两者相结合的第三类—混合调度)。该文研究以最小化Makespan为目标的Flowshop调度问题。基于Agent理论,提出采用Flowshop复合代理体(Flowshop-Compound-Agent,FSCA)求解Flowshop调度问题的方法。在给出FSCA的结构及其实现的基础上,通过毛纺企业制条车间的实例说明了使用FSCA解决Flowshop调度问题的有效性。  相似文献   

3.
柔性Job shop集成化计划调度模型及其求解算法   总被引:8,自引:0,他引:8       下载免费PDF全文
考虑不同加工工艺路径的成本因素,从集成化的角度研究了柔性Job shop计划和调度问题,针对问题的结构特点,建立了两层混合整数规划模型,提出门槛接受,遗传算法与启发式规则相结合的混合求解算法,综合考虑各层次决策问题进行求解,实例计算表明,该算法可迅速求得问题的近优解,表现出良好的求解性能。  相似文献   

4.
基于免疫和模拟退火原理的柔性JobShop调度研究*   总被引:1,自引:1,他引:0  
为了研究柔性Job-Shop调度的不同解法,采用免疫和模拟退化原理求解柔性Job-Shop调度问题。研究了柔性处理问题,提出两种调度策略;分析了算法混合的思想,提出了免疫模拟退火算法。分别采用不同调度策略,使用不同调度算法对多种国际标准算例进行了仿真,仿真结果表明,该模型、策略和算法能够解决柔性Job-Shop调度问题。  相似文献   

5.
方远  李继云等 《计算机工程》2002,28(9):204-206,237
生产调度问题,一般可根据生产流程的不同分为Job-shop调度和Flowshop调度两大类(也有学者认为,存在两者相结合的第三类-混合调度)。该文研究以最小化Makespan为目标的Flowshop调度问题。基于Agent理论,提出采用Flowshop复合代理体(Flowshop-Compond-Agent,FSCA)求解Flowshop调度问题的方法,在给出FSCA的结构及其实现的基础上,通过毛纺企业制度车间的实例说明了使用FSCA解决Flowhop调度问题的有效性。  相似文献   

6.
针对高校教室调度问题进行了研究,综合考虑教室集中时间利用率和学生需求,采用三元组方式,用任务表示课程,用设备表示不同类型的教室。据此,教室排课问题被描述为一类以最小化Cmax与滞后时间和为调度目标,具有机器适用限制的并行机调度问题。然后结合问题特性,建立对应的运筹学调度模型,并运用混合粒子群算法求解该类调度问题。最后仿真结果表明实现了所讨论的两个优化调度目标,获得了满意解;同时通过与其他算法解的比较,得出混合粒子群算法非常适合求解这里所讨论的教室排课问题这一结论。  相似文献   

7.
将粒子群算法运用于求解柔性作业车间调度问题,采用基于轮盘赌的编码方法以及基于邻域互换的局部搜索方法。通过两个不同规模算例的试验计算,与基于粒子位置取整的编码方法进行对比分析,说明了轮盘赌编码方法求解柔性作业车间调度问题的有效性。且采用该编码方法的混合粒子群算法在求解柔性作业车间调度问题时具有更好的求解性能。  相似文献   

8.
传统的优化算法在求解面对多目标柔性作业车间调度时,往往求解效率低且难以获得最优解。为了求解多目标柔性作业车间调度问题,设计了混合人工蜂群算法。种群的初始化采用了多种方法相结合的策略。在人工蜂群算法的不同阶段采用不同的搜索机制,在雇佣蜂阶段采用开发搜索,针对跟随蜂阶段蜜蜂跟随的对象的优秀解进行小幅度的更新,从而提高了搜索的表现。禁忌搜索与改进的人工蜂群算法相结合,有效的提升了获得最优解的概率。通过相关文献中的标准实例对设计的混合人工蜂群算法进行一系列求解测试,实验的结果有效的说明了算法在求解柔性作业车间调度问题时效果显著。通过求解结果对比表明人工蜂群算法的高效性和优越性。  相似文献   

9.
本文针对混合流水车间调度问题,以最大流程时间最小为目标函数,建立了混合整数数学规划模型;将具有解决复杂组合优化问题的免疫克隆选择算法(ICA)应用于求解混合流水车间调度问题,详细描述了ICA算法求解HFSP问题的步骤;为了验证算法的有效性,仿真对比了遗传算法和ICA算法的性能,与文献结果比较,结果表明ICA算法求解HFSP问题可行性和有效性。  相似文献   

10.
基于NSGA2算法的混合流水车间多目标调度问题研究   总被引:1,自引:0,他引:1  
针对混合流水车间多目标调度问题,以最大流程时间和生产中所消耗的总能量最小为目标函数,建立了混合整数数学规划模型;将具有解决复杂组合优化问题的非劣排序遗传算法2(NSGA2)应用于求解多目标混合流水车间调度问题,详细描述了NSGA2算法求解HFSP问题的步骤。利用Matlab仿真,结果表明,NSGA2算法求解多目标HFMSP问题可行性和有效性。  相似文献   

11.
This paper addresses a sub-population based hybrid monkey search algorithm to solve the flow shop scheduling problem which has been proved to be non-deterministic polynomial time hard (NP-hard) type combinatorial optimization problems. Minimization of makespan and total flow time are the objective functions considered. In the proposed algorithm, two different sub-populations for the two objectives are generated and different dispatching rules are used to improve the solution quality. To the best of our knowledge, this is the first application of monkey search algorithm to solve the flow shop scheduling problems. The performance of the proposed algorithm has been tested with the benchmark problems addressed in the literature. Computational results reveal that the proposed algorithm outperforms many other heuristics and meta-heuristics addressed in the literature.  相似文献   

12.
This paper investigates an integrated optimisation problem of production scheduling and preventive maintenance (PM) in a two-machine flow shop with time to failure of each machine subject to a Weibull probability distribution. The objective is to find the optimal job sequence and the optimal PM decisions before each job such that the expected makespan is minimised. To investigate the value of integrated scheduling solution, computational experiments on small-scale problems with different configurations are conducted with total enumeration method, and the results are compared with those of scheduling without maintenance but with machine degradation, and individual job scheduling combined with independent PM planning. Then, for large-scale problems, four genetic algorithm (GA) based heuristics are proposed. The numerical results with several large problem sizes and different configurations indicate the potential benefits of integrated scheduling solution and the results also show that proposed GA-based heuristics are efficient for the integrated problem.  相似文献   

13.
贺利军  李文锋  张煜 《控制与决策》2020,35(5):1134-1142
针对现有多目标优化方法存在的搜索性能弱、效率低等问题,提出一种基于灰色综合关联分析的多目标优化方法.该多目标优化方法采用单目标优化算法构建高质量的参考序列,计算参考序列与优化解的目标函数值序列之间的灰色综合关联度,定义基于灰色综合关联度的解支配关系准则,将灰色综合关联度作为多目标优化算法的适应度值.以带顺序相关调整时间的多目标流水车间调度问题作为应用对象,建立总生产成本、最大完工时间、平均流程时间及机器平均闲置时间的多目标函数优化模型.提出基于灰色关联分析的多目标烟花算法,对所建立的多目标优化模型进行优化求解.仿真实验表明,所提出多目标烟花算法的性能优于3种基于不同多目标优化方法的烟花算法及两种经典多目标算法,验证了所提出的多目标优化方法及多目标算法的可行性和有效性.  相似文献   

14.
Flow shop scheduling problem consists of scheduling given jobs with same order at all machines. The job can be processed on at most one machine; meanwhile one machine can process at most one job. The most common objective for this problem is makespan. However, multi-objective approach for scheduling to reduce the total scheduling cost is important. Hence, in this study, we consider the flow shop scheduling problem with multi-objectives of makespan, total flow time and total machine idle time. Ant colony optimization (ACO) algorithm is proposed to solve this problem which is known as NP-hard type. The proposed algorithm is compared with solution performance obtained by the existing multi-objective heuristics. As a result, computational results show that proposed algorithm is more effective and better than other methods compared.  相似文献   

15.
This paper studies multi-objective flow shop scheduling problems with interfering jobs. That is, there are two sets of jobs and each of which has its own objective. Some jobs are scheduled so as to minimize makespan while the others are to minimize total tardiness. In this case, the problem was mathematically modeled by a mixed integer linear program. Then, a novel biogeography-based optimization was developed to solve the problem. To evaluate the algorithm, its performance was compared with three well-known algorithms in the literature. The results of the present study show that the proposed algorithm outperforms the other tested algorithms.  相似文献   

16.
针对制造型企业普遍存在的流水车间调度问题,建立了以最小化最迟完成时间和总延迟时间为目标的多目标调度模型,并提出一种基于分解方法的多种群多目标遗传算法进行求解.该算法将多目标流水车间调度问题分解为多个单目标子问题,并分阶段地将这些子问题引入到算法迭代过程进行求解.算法在每次迭代时,依据种群的分布情况选择各子问题的最好解及与其相似的个体分别为当前求解的子问题构造子种群,通过多种群的进化完成对多个子问题最优解的并行搜索.通过对标准测试算例进行仿真实验,结果表明所提出的算法在求解该问题上能够获得较好的非支配解集.  相似文献   

17.
The problem of scheduling in permutation flow shop with the objective of minimizing the maximum completion time, or makespan, is considered. A new ant colony optimization algorithm is developed for solving the problem. A novel mechanism is employed in initializing the pheromone trails based on an initial sequence. Moreover, the pheromone trail intensities are limited between lower and upper bounds which change dynamically. When a complete sequence of jobs is constructed by an artificial ant, a local search is performed to improve the performance quality of the solution. The proposed ant colony algorithm is applied to Taillard’s benchmark problems. Computational experiments suggest that the algorithm yields better results than well-known ant colony optimization algorithms available in the literature.  相似文献   

18.
This paper addresses the problem of making sequencing and scheduling decisions for n jobs–m-machines flow shops under lot sizing environment. Lot streaming (Lot sizing) is the process of creating sub lots to move the completed portion of a production sub lots to down stream machines. There is a scope for efficient algorithms for scheduling problems in m-machine flow shop with lot streaming. In recent years, much attention is given to heuristics and search techniques. Evolutionary algorithms that belong to search heuristics find more applications in recent research. Genetic algorithm (GA) and hybrid genetic algorithm (HEA) also known as hybrid evolutionary algorithm fall under evolutionary heuristics. On this concern this paper proposes two evolutionary algorithms namely, GA and HEA to evolve best sequence for makespan/total flow time criterion for m-machine flow shop involved with lot streaming and set-up time. The following two algorithms are used to evaluate the performance of the proposed GA and HEA: (i) Baker's algorithm (BA), an optimal solution procedure for two-machine flow shop problem with lot streaming and makespan objective criterion and (ii) simulated annealing algorithm (SA) for m-machine flow shop problem with lot streaming and makespan and total flow time criteria.  相似文献   

19.
Motivated by applications in iron and steel industry, we consider a two-stage flow shop scheduling problem where the first machine is a batching machine subject to the blocking constraint and the second machine is a discrete machine with shared setup times. We show that the problem is strongly NP-hard when the objective is to minimize the makespan. When solved with a heuristic priority rule, the worst case ratio with the minimum makespan is 2. For a more general objective, the minimization of a linear combination of the makespan and the total blocking time, a quadratic mixed integer program is presented first. Then we pinpoint two cases with polynomial time algorithms: the case without blocking constraint and the case with a given job sequence. Also for the general objective, we analyze an approximation algorithm. Finally, we evaluate the algorithms, giving experimental results on randomly generated test problems.  相似文献   

20.
This paper deals with a stochastic group shop scheduling problem. The group shop scheduling problem is a general formulation that includes the other shop scheduling problems such as the flow shop, the job shop and the open shop scheduling problems. Both the release date of each job and the processing time of each job on each machine are random variables with known distributions. The objective is to find a job schedule which minimizes the expected makespan. First, the problem is formulated in a form of stochastic programming and then a lower bound on the expected makespan is proposed which may be used as a measure for evaluating the performance of a solution without simulating. To solve the stochastic problem efficiently, a simulation optimization approach is developed that is a hybrid of an ant colony optimization algorithm and a heuristic algorithm to generate good solutions and a discrete event simulation model to evaluate the expected makespan. The proposed approach is tested on instances where the random variables are normally, exponentially or uniformly distributed and gives promising results.  相似文献   

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