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61.
以往对竞争性选址问题的研究,都是从服务提供者的角度考虑问题,寻求市场份额最大化或成本最小化的选址方案.实际中,竞争性选址方案的确定应该是服务提供者与服务需求者上下两层关系互动的过程,并且仅仅考虑市场份额最大化或成本最小化是不够的,企业往往更关心的是利润.针对寡头垄断市场配送中心的选址问题,建立基于双层规划的竞争性选址模型,考虑属于不同企业的配送中心对于配送服务需求者的竞争问题,上层目标函数追求配送中心的利润最大化,下层目标函数是配送服务需求者的广义费用最小.给出了遗传算法求解规模较大的问题,通过一个算例验证模型和算法的可行性. 相似文献
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车间设备布局问题,是一个NP难问题.许多学者利用启发式算法来求解并取得了一定的成果,但在模型中存在距离定义不合理的情况,均采用绝对距离的方式,存在不符合实际和计算不准确的情况;本文对距离的定义进行了改进,并利用遗传算法对改进后的模型进行求解,效果良好,达到了预期目标. 相似文献
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特征选择是模式识别中的一个关键问题。在很多实际问题中,很难找到那些最重要的特征,或受条件限制不能对它们进行测量,从而使特征选择成为模式识别系统最困难的任务之一。遗传算法作为一种智能化的优化方法,具有适合特征选择的优势。对遗传算法做了简约介绍,通过相应的程序并结合汽车故障实例进行了分析。 相似文献
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This paper presents the joint optimization of signal setting parameters and dynamic user equilibrium (DUE) traffic assignment for the congested urban road network. The simulation-based approach is employed to obtain the DUE condition for the case of multiple-origin multiple-destination traffic flows. The dynamic traffic assignment simulation program (DTASP), developed in C language is used to assign the traffic dynamically on the road network, whereas method of successive averages (MSA) is modified and used to arrive at the DUE condition. The artificial intelligence technique of genetic algorithms (GAs) is applied to obtain the optimal signal setting parameters and path flow distribution factor for DUE condition. The methodology developed in such a way that joint optimization of signal setting parameters with DUE is obtained. The proposed method is applied to the real network data of Fort Area of Mumbai city comprising of 17 nodes and 56 unidirectional links with 72 Origin–Destination pairs, where all the 17 nodes are signalized intersections. The traffic flow condition for the optimized signal setting parameters is considerably improved compared to the existing signal settings. The results prove that the GA is an effective technique to solve the joint optimization problem for the real network data. 相似文献
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《Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility》2012,50(10):697-733
A design methodology for mechatronic vehicles is presented. With multidisciplinary optimization (MDO) methods, strongly coupled mechanical, control and other subsystems are integrated as a synergistic vehicle system. With genetic algorithms (GAs) at the system level, the mechanical, control and other relevant parameters can be optimized simultaneously. To demonstrate the feasibility and efficacy of the proposed design methodology for mechatronic vehicles, it is used to resolve the conflicting requirements for ride comfort, suspension working spaces and unsprung mass dynamic loads in the optimization of half-vehicle models with active suspensions. Both deterministic and random road excitations, both rigid and flexible vehicle bodies and both perfect measurement of full state variables and estimated limited state variables are considered. Numerical results show that the optimized vehicle systems based on the methodology have better overall performance than those using the linear quadratic Gaussian (LQG) controller. It is shown that the methodology is suitable for complex design optimization problems where: (1) there is interaction between different disciplines or subsystems; (2) there are multiple design criteria; (3) there are multiple local optima; (4) there is no need for sensitivity analysis for the optimizer at the system level; and (5) there are multiple design variables. 相似文献