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《计算机集成制造系统》2017,(1)
传统列生成方法在求解乘务调度问题时,由于搜索二叉树的节点数呈指数级增长使其难以解决大规模问题。为避免搜索整个树节点,提出一种逐次缩小问题规模的迭代优化方法。针对乘务调度问题提出带有换班机会选择的最小费用网络流模型。利用Dantzig-Wolfe分解原理,将该模型转化为带有换班机会选择的集覆盖模型,并采取列生成方法求解其线性松弛解,以得到原问题的下界。在求解整数解时,利用线性松弛解信息,逐次确定不被使用的换班机会集,将问题转化为一系列规模逐次缩小的乘务调度问题。对城市公交中的多组乘务调度实例进行计算,将结果与问题下界和常用遗传算法的结果进行比较,表明大多数实例都能在合理的时间内取得最优解或近优解。 相似文献
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《计算机集成制造系统》2017,(3)
为满足工厂—车间一体化管理需求,研究了不确定可重入定点装配车间生产计划与调度集成优化问题。在分析车间装配特点的基础上,利用期望值描述不确定可重入情况,建立了双层生产计划与调度集成优化随机期望值模型,上层为能力约束的生产计划模型,下层为不确定可重入定点装配车间调度模型。提出了一种具有双层结构的交替迭代式改进遗传算法,上层用精英遗传算法求解生产计划,代入下层后采用基于随机模拟技术的遗传模拟退火算法求解生产调度,然后将调度结果返回上层重新求解新计划,如此不断交替迭代以实现计划与调度的同时优化。通过算例仿真验证了模型及算法的有效性。为制定不确定可重入定点装配车间生产计划与调度提供了一种合理可行的方法。 相似文献
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虚拟企业中细粒度协同设计任务的不确定调度及GA求解 总被引:2,自引:0,他引:2
不确定性资源受限项目调度(URCPSP)是目前研究的热点,处理时间为区间数的任务调度属于URCPSP.针对虚拟企业中细粒度协同设计任务的不确定调度,文中建立了其模型,提出了采用"可能度水平"的方法将其转化为确定性规划,并采用遗传算法进行求解,给出了该遗传算法的结构,并提出了"染色体按资源分段、段内按任务序排列"的编码等具体操作方法.通过实例来说明该不确定调度的具体实现方法,分析表明该方法具有较好的参考价值. 相似文献
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为解决传统鲁棒在处理风电不确定性上存在过度保守的问题,将弱鲁棒原理应用到含风电电力系统调度中。用区间鲁棒不确定集合描述风场功率预测误差的不确定性,并建立了风电参与辅助调频情况下的风场备用、传统机组备用等约束条件,提出了一种考虑风电辅助调频的弱鲁棒经济调度模型,通过调度传统机组和风电场以保证系统安全运行,同时满足了一定经济恶化容忍度;在含风电场的IEEE30节点算例中,对比分析了基准场模型、传统鲁棒模型和弱鲁棒调度模型三者的调度结果。算例结果表明:考虑辅助调频能够提高调度解的可行度,且与传统鲁棒模型相比,弱鲁棒具有更好的经济性。 相似文献
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以园区微电网PMG为研究背景,从日前这一时间尺度出发,建立以PMG日运行成本最小为目标的优化调度模型。根据PMG内可中断负荷预测曲线划分时段,对分时电价TOU进行调整,得出优化后的TOU曲线。通过电价激励用户侧调整部分负荷的用电时段,降低负荷峰谷差,降低购电成本;在调度过程中,PMG与主网通过联络线互联,进行电力交易,使PMG获得收益,进一步提高PMG运行的经济性。将所建立的PMG单目标优化调度模型用灰狼算法求解。仿真结果表明,该日前优化调度模型可有效降低PMG负荷峰谷差,同时提高微电网运行的经济性,验证了该日前优化调度策略的合理性。 相似文献
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针对单机供应链调度问题,在生产计划和批次配送阶段分别考虑分时电价政策和时变行程时间。以总成本最小为目标建立混合整数规划模型,通过对模型的分析给出了最优解的性质,以此将模型分解为若干个批次的机器调度子问题。对于子问题优化,设计了子集划分启发式算法并证明了算法的最优性。对于主问题的优化,设计了自适应变邻域搜索算法。数值计算结果验证了模型和算法的有效性,证明了供应链集成调度能减少大量的能源消耗。 相似文献
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A robust design for a closed-loop supply chain network under an uncertain environment 总被引:2,自引:2,他引:0
Majid Ramezani Mahdi Bashiri Reza Tavakkoli-Moghaddam 《The International Journal of Advanced Manufacturing Technology》2013,66(5-8):825-843
This paper presents a robust design for a multi-product, multi-echelon, closed-loop logistic network model in an uncertain environment. The model includes a general network structure considering both forward and reverse processes that can be used in various industries, such as electronics, digital equipment, and vehicles. Because logistic network design is a time consuming and costly project as well as a strategic and sensitive decision (i.e. the change of such decision is difficult in the future), a robust optimisation approach is adopted to cope with the uncertainty of demand and the return rate described by a finite set of possible scenarios. Hence, to obtain robust solutions with better time, the scenario relaxation algorithm is employed for the proposed model. Numerical examples and a sensitivity analysis are presented to demonstrate the significance and applicability of the presented model. It is shown that solutions resulted from the suggested approach insure more situations, especially in worst case ones. The results show that although the profit values of the robust configuration are less than the deterministic configuration, the robust configuration is more reliable than the deterministic one because the deterministic configuration is infeasible under some demand and return rates (i.e. in the worst cases). Moreover, the results show the computing time superiority of the algorithm compared to the extensive form model as well as optimality of the resulted solutions. 相似文献
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Robust updating of uncertain damping models in structural dynamics for low- and medium-frequency ranges 总被引:1,自引:0,他引:1
This paper deals with the robust updating of uncertain computational models in the context of structural dynamics in the low- and medium-frequency ranges of composite sandwich panels for which experimental results are available. The uncertain computational model is constructed using the non-parametric probabilistic approach which takes into account model and data uncertainties. The formulation of the robust updating problem includes the effects of uncertainties and consists in minimizing a cost function with respect to an admissible set of updating parameters. Updating is performed in two steps using several cost functions and experimental results. The results of the robust updating problem show that the method proposed is efficient for updating the uncertain computational model in both low- and medium-frequency ranges. 相似文献
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In this paper, we consider fuzzy identification of uncertain nonlinear systems in Takagi-Sugeno (T-S) form for the purpose of robust fuzzy control design. The uncertain nonlinear system is represented using a fuzzy function having constant matrices and time varying uncertain matrices that describe the nominal model and the uncertainty in the nonlinear system respectively. The suggested method is based on linear programming approach and it comprises the identification of the nominal model and the bounds of the uncertain matrices and then expressing the uncertain matrices into uncertain norm bounded matrices accompanied by constant matrices. It has been observed that our method yields less conservative results than the other existing method proposed by S?krjanc et al. (2005) [11] and [12]. With the obtained fuzzy model, we showed the robust stability condition which provides a basis for different robust fuzzy control design. Finally, different simulation examples are presented for identification and control of uncertain nonlinear systems to illustrate the utility of our proposed identification method for robust fuzzy control. 相似文献
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Mohammad Ebrahim Nikoofal Seyed Jafar Sadjadi 《The International Journal of Advanced Manufacturing Technology》2010,50(1-4):391-397
In this paper, we propose a p-median problem with uncertain edge lengths where uncertainty is characterized by given intervals. The uncertainty in edge lengths may appear in transportation costs or travel times along the edges in any network location problem. Minimax regret approach is a promising tool to cope with uncertainty in network location problems. However, minimax regret algorithms normally suffer from complexity, and they are time consuming. We propose a robust optimization approach to obtain the robust linear counterpart for the same class of the nominal p-median problem. The performance of the proposed model is compared with minimax regret approach through a simple but illustrative example, and results are discussed in more details. 相似文献
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Under operational conditions, some loads acting on a beam are known (deterministic loads), but there usually exist other loads the magnitude and distribution of which are unpredictable (uncertain loads). If the uncertainty in the loading is not taken into account in the design, the likelihood of failure increases. In the present study beams are designed for minimum weight subject to maximum stress and buckling load criteria and under deterministic and uncertain transverse loads. The uncertain load, which is subject to a constraint on its L 2 norm, is determined to maximize the normal stress using a convex analysis. The location of the maximum stress is determined under the combination of deterministic and worst-case uncertain loads. The minimum weight design is obtained by determining the minimum cross-sectional area subject to stress and buckling load constraints. Results are given for a number of problem parameters including the axial load, elastic foundation modulus and uncertainty levels. 相似文献
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对凸集不确定性和随机变量共存的结构混合可靠性模型行研究,以解决部分参量统计信息不足时的结构可靠性评定问题。基于Info-gap理论,建立一种统一的结构非概率可靠性模型,由此导出一种与概率可靠性方法等价的椭球非概率可靠性模型。用一种特定的椭球凸集模型描述随机变量不确定性,与一般性的凸集模型复合,将凸集不确定性和随机变量共存的混合可靠性问题统一为非概率可靠性问题。基于非概率可靠性方法,提出一种一般性的凸集-概率混合可靠性方法。给出的混合可靠性指标同时具有稳健可靠性和概率可靠性意义,可通过含约束的优化问题求解。算例分析显示,当数据分散性较强或较弱时,已有的混合可靠性方法不能有效度量结构可靠性,新方法更具合理性和适用性。 相似文献
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基于FORM的齿轮传动多学科优化设计 总被引:4,自引:0,他引:4
通常多学科设计优化是一种确定性设计方法,未考虑不确定性因素的影响。为降低多学科设计优化过程中不确定性因素对系统性能的影响,将一次可靠性方法与协同优化方法相结合,应用到多学科设计优化中。建立基于一次可靠性方法的协同优化的数学模型,并阐述其求解流程,该方法可用于多学科设计优化领域的可靠性设计问题。分别运用协同优化方法和基于一次可靠性方法的协同优化实现了减速器齿轮传动的多学科优化设计,在这两种方法的系统级优化中,引入松弛变量,将一致性等式约束转化为不等式约束,使算法易于收敛。优化结果表明基于一次可靠性方法的协同优化方法求得的最优解使得约束条件满足了可靠性要求,提高了系统的可靠性,具有实际工程意义。 相似文献