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针对混合流水车间绿色生产过程中的设备选择和调度目标匹配问题,提出基于机床加工特性的多目标调度模型和改进遗传算法。该算法建立了混合流水车间调度的时间、能耗与成本优化模型,采用模糊隶属方法描述了机床加工特性,在遗传算法求解过程中通过机床加工特性隶属度与调度目标的权重系数匹配关系,建立了自适应的交叉、变异和优势保留策略,在每一代迭代中提高在调度目标方向上的选择压力,加速收敛。通过实例分析对比了不同算法的优化结果,从而验证了模型及算法的有效性,并提出了高效、节能、经济和综合4种调度生产模式,为混合流水车间绿色生产提供了指导。 相似文献
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多工艺路线的批量生产调度优化 总被引:14,自引:0,他引:14
以优化生产周期为目标,研究了多工艺路线的批量调度问题,提出了一种基于工序优先级的调度算法,并将该算法嵌入到遗传算法中,得到了全局优化的批量调度算法。遗传算法搜索最佳染色体,调度算法把染色体解码为调度。在调度算法中,采用了3种提高生产率的策略,即区分批量启动时间与工序加工时间,在工件到达机床之前做好准备工作;把一批工件分成多个小生产批次,每批次独立加工:一批工件加工部分后就运向后续加工机床,缩小后续机床的等待时间。仿真表明,该调度方法能取得较好结果。 相似文献
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以带有控制器的Petri网为建模工具对柔性生产调度中的离散事件建模,利用遗传算法和模拟退火算法获得调度结果,并通过Petri网进行控制.用于解决作业车间的加工受到机床、操作工人等生产资源制约条件下的优化调度.以生产周期为目标进行的优化调度,将遗传算法和模拟退火相结合.通过多种交叉、变异、概率更新选择、再分配策略等遗传和模拟操作,得到目标的最优或次优解.对算法进行了仿真研究,仿真结果表明该算法是有效性. 相似文献
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对具有模糊加工时间和模糊交货期的多工艺路线的作业车间调度问题进行了研究;以最大化平均满意度为调度目标,建立了作业车间模糊调度的数学模型,提出了一种基于遗传算法的全局优化的调度算法;设计了包含工序及其加工机床信息的染色体编码,对染色体的解码方法、交叉方法和变异方法进行了研究。仿真结果表明,该算法是可行的,与其他同类研究相比,有一定的优越性。 相似文献
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为解决具有多目标约束的Job-shop问题,提出了一种利用模糊综合评判规则和优先分配启发式算法相结合的调度算法.首先,用层次分析法给出各评判目标的评价权重;再由模糊综合评判规则确定目标权重下零件各工序所用的加工机床;最后,利用优先分配启发式调度算法确定在同一台机床上加工的各零件的先后顺序.实验结果证明了算法的有效性. 相似文献
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为解决低碳策略下多目标柔性作业车间调度问题,在深入分析柔性作业车间多目标调度研究现状和不足的基础上,结合基于设备状态—能耗曲线的低碳策略,提出包括能源消耗、最大完工时间、加工成本和成本加权加工质量的多目标柔性作业调度模型。针对上述模型,设计了基于血缘变异的改进非支配排序遗传算法,该算法根据计算交叉染色体的血缘关系确定变异率,优化了交叉和变异策略,解决了算法的早熟问题。针对具体实例,构建了调度模型和算法,计算结果验证了算法的可行性和有效性。 相似文献
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为解决具有多目标约束的Job-shop问题,提出了一种利用模糊综合评判规则和优先分配启发式算法相结合的调度算法。首先,用层次分析法给出各评判目标的评价权重;再由模糊综合评判规则确定目标权重下零件各工序所用的加工机床;最后,利用优先分配启发式调度算法确定在同一台机床上加工的各零件的先后顺序。实验结果证明了算法的有效性。 相似文献
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针对考虑工件移动时间约束的柔性作业车间调度问题,构建了以加工总成本和最大加工时间最小为目标的数学模型并用改进遗传算法求解。针对柔性作业车间调度问题(FJSP)特性,算法中采用基于工序的集成编码操作,实现工序排序和机器匹配的内在关联并由此产生可行的调度方案;根据编码结构设计了有效的交叉和变异操作,从而避免了非法调度解的出现;为克服遗传算法的早熟收敛和减少调度开销,用贪婪解码算法生成主动调度、设计了自适应变异规则并采用混合子代产生模式提高染色体适应值。最后通过测试问题的求解及数值分析,证明了算法和模型的有效性及鲁棒性。 相似文献
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多目标批量生产柔性作业车间优化调度 总被引:14,自引:0,他引:14
研究批量生产中以生产周期、最大提前/最大拖后时间、生产成本以及设备利用率指标(机床总负荷和机床最大负荷)为调度目标的柔性作业车间优化调度问题。提出批量生产优化调度策略,建立多目标优化调度模型,结合多种群粒子群搜索与遗传算法的优点提出具有倾向性粒子群搜索的多种群混合算法,以提高搜索效率和搜索质量。仿真结果表明,该模型及算法较目前国内外现有方法更为有效和合理。最后,从现实生产实际出发给出多目标批量生产柔性调度算例,结果可行,可对生产实践起到一定的指导作用。 相似文献
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S. Deva Prasad C. Rajendran O. V. Krishnaiah Chetty 《The International Journal of Advanced Manufacturing Technology》2006,29(5-6):564-576
In this paper, we consider the problem of extended permutation flowshop scheduling with the intermediate buffers. The Kanban
flowshop problem considered involves dual-blocking by both part type and queue size acting on machines, as well as on material
handling. The objectives considered in this study include the minimization of mean completion time of containers, mean completion
time of part types, and the standard deviation of mean completion time of part types. An attempt is made to solve the multi-objective
problem by using a proposed genetic algorithm, called the “non-dominated and normalized distanceranked sorting multi-objective
genetic algorithm” (NDSMGA). In order to evaluate the NDSMGA, we have made use of randomly generated flowshop scheduling problems
with input and output buffer constraints in the flowshop. The non-dominated solutions for these problems are obtained from
each of the existing methods, namely multi-objective genetic local search (MOGLS), elitist non-dominated sorting genetic algorithm
(ENGA), gradual priority weighting genetic algorithm (GPWGA), modified MOGLS, and the NDSMGA. These non-dominated solutions
are combined to obtain a net non-dominated solution set for a given problem. Contribution in terms of number of solutions
to the net non-dominated solution set from each of these algorithms is tabulated, and the results reveal that a substantial
number of non-dominated solutions are contributed by the NDSMGA. 相似文献
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S. Deva Prasad O.V. Krishnaiah Chetty C. Rajendran 《The International Journal of Advanced Manufacturing Technology》2006,29(5):564-576
In this paper, we consider the problem of extended permutation flowshop scheduling with the intermediate buffers. The Kanban
flowshop problem considered involves dual-blocking by both part type and queue size acting on machines, as well as on material
handling. The objectives considered in this study include the minimization of mean completion time of containers, mean completion
time of part types, and the standard deviation of mean completion time of part types. An attempt is made to solve the multi-objective
problem by using a proposed genetic algorithm, called the “non-dominated and normalized distance-ranked sorting multi-objective
genetic algorithm” (NDSMGA). In order to evaluate the NDSMGA, we have made use of randomly generated flowshop scheduling problems
with input and output buffer constraints in the flowshop. The non-dominated solutions for these problems are obtained from
each of the existing methods, namely multi-objective genetic local search (MOGLS), elitist non-dominated sorting genetic algorithm
(ENGA), gradual priority weighting genetic algorithm (GPWGA), modified MOGLS, and the NDSMGA. These non-dominated solutions
are combined to obtain a net non-dominated solution set for a given problem. Contribution in terms of number of solutions
to the net non-dominated solution set from each of these algorithms is tabulated, and the results reveal that a substantial
number of non-dominated solutions are contributed by the NDSMGA. 相似文献
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Scheduling flowshops with condition-based maintenance constraint to minimize expected makespan 总被引:1,自引:1,他引:0
Ehram Safari Seyed Jafar Sadjadi Kamran Shahanaghi 《The International Journal of Advanced Manufacturing Technology》2010,51(5-8):757-767
Flexible job-shop scheduling problem (FJSP) is an extended traditional job-shop scheduling problem, which more approximates to practical scheduling problems. This paper presents a multi-objective genetic algorithm (MOGA) based on immune and entropy principle to solve the multi-objective FJSP. In this improved MOGA, the fitness scheme based on Pareto-optimality is applied, and the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Efficient crossover and mutation operators are proposed to adapt to the special chromosome structure. The proposed algorithm is evaluated on some representative instances, and the comparison with other approaches in the latest papers validates the effectiveness of the proposed algorithm. 相似文献
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Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem 总被引:1,自引:1,他引:0
Seyed Habib A. Rahmati M. Zandieh M. Yazdani 《The International Journal of Advanced Manufacturing Technology》2013,64(5-8):915-932
The aim of this paper is to study multi-objective flexible job shop scheduling problem (MOFJSP). Flexible job shop scheduling problem is a modified version of job shop scheduling problem (JSP) in which an operation is allowed to be processed by any machine from a given set of capable machines. The objectives that are considered in this study are makespan, critical machine work load, and total work load of machines. In the literature of the MOFJSP, since this problem is known as an NP-hard problem, most of the studies have developed metaheuristic algorithms to solve it. Most of them have integrated their objective functions and used an integrated single-objective metaheuristic algorithm though. In this study, two new version of multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm are adapted for MOFJSP. These algorithms use new multi-objective Pareto-based modules instead of multi-criteria concepts to guide their process. Another contribution of this paper is introducing of famous metrics of the multi-objective evaluation to literature of the MOFJSP. A new measure is also proposed. Finally, through using numerous test problems, calculating a number of measures, performing different statistical tests, and plotting different types of figures, it is shown that proposed algorithms are at least as good as literature’s algorithm. 相似文献
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S. Nima Mirabedini Hossein Iranmanesh 《The International Journal of Advanced Manufacturing Technology》2014,70(9-12):1579-1589
In factories during production, preventive maintenance (PM) scheduling is an important problem in preventing and predicting the failure of machines, and most other critical tasks. In this paper, we present a new method of PM scheduling in two modes for more precise and better machine maintenance, as pieces must be replaced or be repaired. Because of the importance of this problem, we define multi-objective functions including makespan, PM cost, variance tardiness, and variance cost; we also consider multi-parallel series machines that perform multiple jobs on each machine and an aid, the analytic network process, to weight these objectives and their alternatives. PM scheduling is an NP-hard problem, so we use a dynamic genetic algorithm (GA) (the probability of mutation and crossover is changed through the main GA) to solve our algorithm and present another heuristic model (particle swarm optimization) algorithm against which to compare the GA’s answer. At the end, a numerical example shows that the presented method is very useful in implementing and maintaining machines and devices. 相似文献
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为解决以设备能耗、刀具磨损和切削液消耗为碳排放来源,能耗和人工费用为加工成本的多目标柔性作业车间低碳调度问题,建立以最小化碳排放量、最长完工时间和加工成本为目标的低碳调度模型,提出一种改进带精英策略的非支配遗传算法(NSGA-Ⅱ)并进行求解。首先通过基于Tent混沌映射的编码与融合了层次分析法(AHP)的贪婪解码来动态调整染色体组成,提高初始种群质量;然后提出了一种基于遗传参数的自适应遗传策略,根据种群进化阶段与种群非支配状态动态调整交叉、变异率;最后设计了一种基于外部档案集的改进精英保留策略,提高了算法后期的种群多样性并保留了进化过程中的优质个体。通过标准调度算例与实际案例验证了改进算法的有效性。 相似文献
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混合离散蝙蝠算法求解多目标柔性作业车间调度 总被引:3,自引:0,他引:3
针对以最大完工时间、生产成本和生产质量为目标的柔性作业车间调度问题,在研究和分析蝙蝠算法的基础上,提出一种混合离散蝙蝠算法。为了提高求解多目标柔性作业车间调度问题的混合离散蝙蝠算法的初始种群质量,在通过分析初始选择的机器与每道工序调度完工时间两者关系的基础上,提出一种优先指派规则策略产生初始种群,提高了算法的全局搜索能力。同时采用位置变异策略来使得算法在较短的时间内尽可能多地搜索到最优位置,有效地避免了算法早熟收敛。在计算问题的目标值上面,首次提出时钟算法。针对具体实例进行测试,试验数据表明,该算法在求解柔性作业车间调度问题上有很好的性能,是一种有效的调度算法,从而为解决这类问题提供了新的途径和方法。 相似文献