共查询到20条相似文献,搜索用时 125 毫秒
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绿色制造和智能制造是工业发展的两大趋势,针对目前作业车间能耗大、设备利用率低,以及产品拖期严重等问题,以智能制造业环境中的作业车间为研究对象,建立了以车间总能耗和总拖期惩罚为优化目标的多目标调度模型,并通过设置权重系数来调节优化目标决策偏好;基于遗传算法收敛速度快、全局搜索能力强,以及模拟退火算法突跳性强的特点,设计一种新型的遗传退火算法对问题进行求解.在算法设计中提出新的退火函数,同时结合回火机制,可有效地求出车间总能耗与总拖期惩罚的关系.最后,通过实例验证该模型的可行性和所提算法的有效性. 相似文献
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提前/拖期惩罚的单机批调度优化问题研究 总被引:6,自引:0,他引:6
针对准时生产意义下加工设备的单机作业准时生产方式,研究了提前/拖期惩罚的批调度问题,目标是使得加工总成本最小,目标函数不仅考虑了提前/拖期惩罚,还考虑了机器的加工费用。为了确定任务的最优分批与各批次的开始时间,给出了批调度优化应具有的4个特性,并根据这4个特性提出了两个启发式算法:按序搜索算法和对折搜索算法使得目标函数为最小。最后对两种算法的特点进行了分析。 相似文献
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为解决小批量、多品种浸染生产不合理调度导致高能耗和多污染排放的问题,提出了一种基于遗传算法和多智能体染缸调度与动态优化的方法.该方法基于染缸车间制造执行系统、企业资源计划和过程控制系统实时数据,采用分层调度算法.其中静态层采用支持多产品的批处理、多染缸的非等同性、前期订单、订单交货期和切换成本等约束条件的遗传算法;动态层采用支持染缸运行状态的多智能体的协调动态优化算法.通过对生产过程中多约束条件和多动态变化因素的算法求解,获得染缸作业任务动态优化设计.仿真结果表明,与单纯遗传算法和人工调度相比,基于数据驱动的分层动态优化调度达到了染缸作业排产优化和污染减排的目标和实际应用的可行性. 相似文献
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交货期惩罚下柔性车间调度多目标Pareto优化研究 总被引:1,自引:0,他引:1
针对传统作业车间调度问题的局限性,结合实际生产过程的特点和约束条件,建立路径柔性的作业车间调度仿真模型。采用连续空间蚁群算法,对柔性车间作业进行多变量、多约束下的调度布局优化设计,在考虑各个机器提前/拖期完工的惩罚值,所有机器上的总负荷、成品合格率和最大设备利用率等性能指标更加合理情况下,为每次迭代产生的邻域解集作为Pareto非支配排序,防止算法操作过程中劣解的产生,提高求解效率。并与自适应免疫算法和交换序列混合粒子群法的优化结果进行对比,该算法可有效改善基本蚁群算法的停滞现象和全局寻优能力差的缺点。目前,该方法已在某机械公司进行示范,在提高加工效率、降低生产成本、减少协作费等方面效果显著。 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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求解作业车间调度问题的快速启发式算法 总被引:5,自引:0,他引:5
首先将作业车间调度问题转换为一个搭积木模型,受这个直观模型的启发,提出了一个启发式的搭积木规则,该规则综合考虑了已经搭好的积木的顶高和将要搭积木的剩余高度。基于这个规则,提出了一个求解作业车间调度问题的快速启发式算法,对国际上通用的benchmark例的模拟实验结果表明,提出的算法优于经典的优先分配启发式算法。 相似文献
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针对现有制造系统中协作计划、生产计划和调度方案不能同步制定的问题,考虑在供应链环境下有协作的计划与调度,构建了一种多目标集成协作计划与调度优化模型。提出一种基于Pareto最优的多目标优化算法,设计了包含协作染色体的基于作业的集成编码方案,通过惩罚操作实现协作计划与生产计划的同步协调。考虑供应链协调中常见的完工时问、总成本、总拖期时间、平均流经时间四个性能指标对模型进行整体优化。通过仿真实验验证了模型及其算法的有效性。 相似文献
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根据双向冲压线的实际生产特点,提出了一种基于工序约束并行机的双向冲压线调度模型.在该模型中,工件同时在牛产线两端按设备顺序加工,且加工工件及其加工开始时间和完工时间受生产线两端工件工序数目约束和生产线设备加工能力的约束,给出了该约束的规则;设计了启发规则和遗传算法混合的求解算法.最后,以最大完工时间为优化指标进行验证,证明该模型具有较好的实用价值. 相似文献
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Chang Ouk Kim Hyun Joon Shin 《The International Journal of Advanced Manufacturing Technology》2003,22(3-4):278-287
Many real scheduling problems are often much more complex than problems that are analytically tractable. The main difficulty in obtaining optimal job schedules arises from the existence of sequence dependent setup times among jobs and job release times. In this paper, we present a restricted tabu search algorithm that schedules jobs on parallel machines in order to minimise the maximum lateness of the jobs. The jobs have release times and due dates, and sequence-dependent setup times exist between the jobs. The parallel machines are either identical or non-identical in terms of the processing times of the jobs. The restricted tabu search algorithm employs a restricted search with the elimination of non-effective job moves, for finding the best neighbourhood schedule. The restricted search algorithm reduces search effort significantly while obtaining good quality final schedule. The experimental results show that the proposed algorithm obtains much better solutions more quickly than other heuristic algorithms such as the Rolling Horizon Procedure (RHP) heuristic, the basic tabu search and simulated annealing. 相似文献
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Quan-Ke Pan Ponnuthurai N. Suganthan Tay J. Chua T. X. Cai 《The International Journal of Advanced Manufacturing Technology》2010,46(9-12):1229-1237
Manpower scheduling problem is one of the key scheduling problems with extensive applications in manufacturing. This paper presents a mixed-integer programming model with a two-stage heuristic algorithm for solving the manpower scheduling problem in the precision engineering industry. Firstly, a mixed-integer programming formulation is developed to model the manpower scheduling problem in this high-mix low-volume manufacturing environment. Secondly, a two-stage heuristic algorithm is proposed where the first stage is deployed to calculate the skill requirements for each shift by considering the jobs, machines, and their production schedule and the second stage is designed to assign operators to the machines by considering the skill set requirements and the operator's expressed preferences. Lastly, the computational results based on problem instances emulating real-world scenarios demonstrated the feasibility and effectiveness of the proposed heuristic. 相似文献
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Suk Jae Jeong Kyung Sup Kim 《The International Journal of Advanced Manufacturing Technology》2008,37(7-8):793-802
In order to maximize an availability of machine and utilization of space, the parallel machines scheduling problem with space
limit is frequently discussed in the industrial field. In this paper, we consider the parallel machine scheduling problem
in which n jobs having different release times, due dates, and space limits are to be scheduled on m parallel machines. The objective function is to minimize the weighted sum of earliness and tardiness. To solve this problem,
a heuristic is developed which is divided into three modules hierarchically: job selection, machine selection and job sequencing,
and solution improvement. To illustrate its effectiveness, a proposed heuristic is compared with genetic algorithm (GA), hybrid
genetic algorithm (HGA), and tabu search (TS), which are well-known meta-heuristics in a large number of randomly generated
test problems based on the field situation. Also, we determine the job selection rule that is suitable to the problem situation
considered in this paper and show the effectiveness of our heuristic method. 相似文献
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S.-S. Kim H. J. Shin D.-H. Eom C.-O. Kim 《The International Journal of Advanced Manufacturing Technology》2003,22(9-10):753-760
The effective management of shop floor resources is an important factor in achieving the goals of a manufacturing company. The need for effective scheduling is particularly strong in complex manufacturing environments. This paper presents an efficient due date density-based categorising heuristic to minimise the total weighted tardiness (TWT) of a set of tasks with known processing times, due dates, weights and sequence-dependent setup times for parallel machines scheduling. The proposed heuristic is composed of four phases. In the first phase, jobs are listed by the earliest due date (EDD). The second phase computes the due date gaps between listed jobs and categorises the jobs based on the due date density. In the third phase, the sequence of jobs is improved by a tabu search (TS) method. The fourth phase allocates each job to machines. The comprehensive simulation results show that the proposed heuristic performs better than other existing heuristics at a significantly reduced total weighted tardiness. 相似文献
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We consider the problem of scheduling N jobs on M unrelated parallel machines to minimize maximum tardiness. Each job has
a due date and requires a single stage of processing. A setup for dies is incurred if the type of the job scheduled is different
from the previous one on that machine. For each die type, the number of dies is restricted. Because of the mechanical structure
of the machines and the fitness of dies to each machine, the processing time depends on both the job and the machine. In this
paper, an efficient heuristic based on guided search, record-to-record travel, and tabu lists is presented to minimize maximum
tardiness. Computational characteristics of the proposed heuristic are evaluated through extensive experiments, which show
that the proposed heuristic outperforms a simulated annealing method tested and is able to prescribe the optimal solutions
for problems in small scales. 相似文献
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Muthu Mathirajan V. Bhargav V. Ramachandran 《The International Journal of Advanced Manufacturing Technology》2010,48(9-12):1133-1148
This study considers the scheduling problem observed in the burn-in operation of semiconductor final testing, where jobs are associated with release times, due dates, processing times, sizes, and non-agreeable release times and due dates. The burn-in oven is modeled as a batch-processing machine which can process a batch of several jobs as long as the total sizes of the jobs do not exceed the machine capacity and the processing time of a batch is equal to the longest time among all the jobs in the batch. Due to the importance of on-time delivery in semiconductor manufacturing, the objective measure of this problem is to minimize total weighted tardiness. We have formulated the scheduling problem into an integer linear programming model and empirically show its computational intractability. Due to the computational intractability, we propose a few simple greedy heuristic algorithms and meta-heuristic algorithm, simulated annealing (SA). A series of computational experiments are conducted to evaluate the performance of the proposed heuristic algorithms in comparison with exact solution on various small-size problem instances and in comparison with estimated optimal solution on various real-life large size problem instances. The computational results show that the SA algorithm, with initial solution obtained using our own proposed greedy heuristic algorithm, consistently finds a robust solution in a reasonable amount of computation time. 相似文献
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采用赋时变迁Petri网,建立了一种作业车间调度模型.通过为机器分配工序来消解因机器库所共享而引起的冲突,得到了表示调度方案的标志图,给出了一种生成可行调度标志图的方法.同时,提出了一种变迁激发序列编码的离散版粒子群算法,并将模拟退火算法嵌入到该粒子群算法中,以提高算法的优化性能.仿真结果验证了混合算法的可行性和有效性. 相似文献