Multiple-objective design of an \overline{{\user2{X}}} control chart with multiple assignable causes

Control charts are widely implemented in firms to establish and maintain statistical control of a process which leads to the improved quality and productivity. Therefore, designs of control charts have gained particular attention from the outset. Design of control charts requires that the engineer selects a sample size, a sampling frequency, and the control limits for the chart. In this paper, a possible combination of design parameters is considered as a decision-making unit which is identified by three attributes: hourly expected cost, detection power of the chart, and in-control average run length. Subsequently, optimal design of control charts is formulated as a multiple-objective decision-making problem. Moreover, the cost function is extended from single to multiple assignable causes because there exist multiple assignable causes in real practice. An algorithm using data envelopment analysis is applied to solve the multiple-objective decision-making (MODM) model. Some numerical and experimental analyses are provided to illustrate the algorithm procedure. Sensitivity analysis is carried out to investigate the robustness of the model, and comparisons with other related published papers are made. It is shown that the proposed MODM model can overcome some drawbacks attached to the previous models and approaches.     

Using genetic algorithm and response surface methodology for statistically constrained optimization of VSI X-bar control charts under multiple assignable causes and non-normality

Variable sampling interval (VSI) control charts have been introduced with the aim of improving performance of traditional control charts. Usually, in the economic–statistical design of the VSI $ \overline{X} $ control charts, it is assumed that observations are normally distributed and process is subjected to only one assignable cause. However, in practice these assumptions could easily fail to hold, and results no longer could be realistic. This paper considers non-normal observations for the case of multiple assignable causes to develop a cost model for the economic design of VSI $ \overline{X} $ control chart. Being more applicable for all types of distributions, Burr distribution is employed for representing the distribution of non-normal process data. Since the proposed design consists of a complex nonlinear cost function that cannot be solved using a classical optimization method, genetic algorithm (GA) searching method as an efficient famous metaheuristic is employed to find the optimal values for the design parameters. Moreover, to improve the performances, response surface methodology is employed to calibrate GA parameters. The effectiveness of the proposed scheme is evaluated through a numerical example. Sensitivity analysis is also carried out to show the effects of cost and process parameters on the outputs of the model. Results show that in all cases, presented VSI model has better economical and statistical performances than its corresponding fixed sampling interval scheme.     

基于遗传算法的球度误差评定

首先对球度公差评定问题进行了综述.然后根据圆度公差的数学定义,引申提出球度公差最小区域条件下的评定模型,并给出遗传算法的适应度函数.随后给出算法实现中的中的关键问题.最后用实例对算法进行了检验,计算结果表明基于遗传算法的球度误差优化算法不仅符合最小区域的条件,而且易于理解和实现,能够获得全局最优解,保证了高精度、高效率.     

应用于IID变量的ARMA控制图性能分析

首先从历史数据加权角度对应用于ⅡD（independent identical distribution）变量的ARMA控制图统计量进行了分析,结果表明,ARMA统计量对历史数据处理的灵活性优于文中提到的一元控制图,尤其是对当前数据的处理更能适应不同情况的需要,可以选择不同的控制图参数来提高控制图检测异常原因的能力。而且从统计量的变化形式来看,文中提到的一元控制图均可视为ARMA控制图的特殊情况;其次,提出了具有某ARL要求的,正态分布观测值ARMA控制图的设计方案,并应用蒙特卡罗模拟方法,总结出检测均值一次永久偏移的最优参数取值范围;最后,介绍应用信噪比方法,可对ARMA控制图的参数进行粗略的选择,同时也验证了最优参数取值范围选择的正确性。     

Constrained H2 approximation of multiple input-output delay systems using genetic algorithm

The paper presents a constrained H2 approximation method for multiple input-output delay systems by using a genetic algorithm. The H2 error between the original and the approximate models is minimized subject to constraints on the H(infinity) error between them and the matching of their steady-state under step inputs. In particular, the H2 error is used as the objective (fitness) function for minimization with the best parameters of the approximate model obtained by repeating the genetic operations on the population incorporated a parameter search space expansion scheme. The effectiveness of the proposed method is demonstrated by numerical examples. It is shown that the approximate models obtained by this approach have better approximation performance in both the H2 and H(infinity) norms, as well as the steady-state response, than those obtained by a previous gradient-based minimization approach.     

An adaptive genetic algorithm with multiple operators for flowshop scheduling

Genetic algorithms (GAs) are a class of effective parallel searching algorithms inspired by the idea of “survival of the fittest”, which has been successfully applied to a variety of problems, especially in the fields of manufacturing and scheduling. However, it is reported that traditional GAs often suffer from the weaknesses of premature convergence as well as parameter and operator dependence. So far, many improved methods with adaptive parameters or hybrid structures have been proposed, but there is little literature considering the adaptive control of genetic operators. In this paper, an adaptive GA (AGA) with multiple operators is proposed for flowshop scheduling, which is a typical NP-hard optimisation problem with many industrial applications and has been widely studied in both academic and engineering fields. In AGA, multiple different genetic operators are employed in an adaptive hybrid way to enhance the exploration and exploitation abilities so as to prevent premature convergence and achieve superior performance. It especially important to stress that the utilising ratio of each operator for hybridisation is adaptively and dynamically controlled during the evolutionary searching process. Simulation results based on benchmarks demonstrate the effectiveness of AGA by contrast with traditional GAs. And the effect of the adaptive control of the operator and the effects of some parameters on the optimisation performance are discussed as well.     

Disassembly sequencing using genetic algorithm

At the end-of-life (EOL) of a product, there are several options available for its processing including reuse, remanufacturing, recycling and disposing. In almost all cases, a certain level of disassembly may be necessary. Thus, finding an optimal (or near optimal) disassembly sequence is crucial to increasing the efficiency of the process. Disassembly operations are labor intensive, can be costly, have unique characteristics and cannot be considered as the reverse of assembly operations. Since the complexity of determining the best disassembly sequence increases with increase in the number of parts of the product, it is extremely crucial that an efficient methodology for disassembly sequencing be developed. In this paper, we present a genetic algorithm for disassembly sequencing of EOL products. A case example is considered to demonstrate the functionality of the algorithm.     

Branch-pipe-routing approach for ships using improved genetic algorithm

Branch-pipe routing plays fundamental and critical roles in ship-pipe design. The branch-pipe-routing problem is a complex combinatorial optimization problem and is thus difficult to solve when depending only on human experts. A modified genetic-algorithm-based approach is proposed in this paper to solve this problem. The simplified layout space is first divided into threedimensional (3D) grids to build its mathematical model. Branch pipes in layout space are regarded as a combination of several two-point pipes, and the pipe route between two connection points is generated using an improved maze algorithm. The coding of branch pipes is then defined, and the genetic operators are devised, especially the complete crossover strategy that greatly accelerates the convergence speed. Finally, simulation tests demonstrate the performance of proposed method.     

A genetic algorithm for active noise control actuator positioning

Active noise control systems (ANCS) have been used extensively in several diverse applications in order to reduce the sound levels within a confined space. Several control schemes have been proposed, all of which rely on the same basic idea, namely that of introducing an artificial secondary sound field in order to partially cancel the primary sound field. This secondary field is created by using a number of actuators (usually speakers), placed in several locations within the space of interest. In our particular case of interest, the ANCS under consideration is an interior one. The overall ANCS comprises a set of sensors, a control system, and the actuators. Clearly, the optimal design of all three components needs to be addressed when designing an ANCS. In this paper, the problem of deciding to which specific locations (out of several possible ones) the actuators will be located for optimal performance of the ANCS is addressed. Specifically, a genetic algorithm is designed for this problem. The algorithm was implemented and tested against a simple greedy approach. Results indicate the efficiency of the Genetic Algorithm for the problem at hand.     

Optimization of vendor managed inventory of multiproduct EPQ model with multiple constraints using genetic algorithm

The aim of this paper is to investigate the vendor managed inventory (VMI) problem of a single-vendor single-buyer supply chain system, in which the vendor is responsible to manage the buyer’s inventory. To include an extended applicability in real-world environments, the multiproduct economic production quantity model with backordering under three constraints of storage capacity, number of orders, and available budget is considered. The nonlinear programming model of the problem is first developed to determine the near optimal order quantities along with the maximum backorder levels of the products in a cycle such that the total VMI inventory cost of the system is minimized. Then, a genetic algorithm (GA) based heuristic is proposed to solve the model. Numerical examples are given to both demonstrate the applicability of the proposed methodology and to fine tune the GA parameters. At the end, the performance of the proposed GA is compared to the one of the LINGO software using different problem sizes. The results of the comparison study show that, while the solutions do not differ significantly, the proposed GA reaches near optimum solutions in significantly less amount of CPU time.     

Improved recognition of control chart patterns using artificial neural networks

Recognition of abnormal patterns in control charts provides clues to reveal potential quality problems in the manufacturing processes. One potentially popular approach for recognizing different control chart patterns (CCPs) is to develop heuristics based on various shape features of the patterns. The advantage of this approach is that the users can easily understand how a particular pattern is identified. However, consistency in the recognition performance is found to be considerably poor in the heuristics approach. Since shape features represent the main characteristics of the patterns in a condensed form, artificial neural network (ANN) with features extracted from the process data as input vector representation can facilitate efficient pattern recognition with a smaller network size. In this paper, a set of seven shape features is selected, whose magnitudes are independent of the process mean and standard deviation under a special representation of the sampling interval in the control chart plot. Based on these features, the CCPs are recognized using a multilayered perceptron neural network trained by back-propagation algorithm. The recognizer can recognize all the eight commonly observed CCPs. Extensive performance evaluation of this recognizer is carried out using simulated pattern data. Numerical results indicate that the developed ANN recognizer can perform well in real time process control applications with respect to both recognition accuracy and consistency.     

Performance evaluation and design optimization using differential evolutionary algorithm of the pantograph for the high-speed train

The global trend in the railway industry is the effort to increase the maximum speed and stability of a train. For an electric railway vehicle to meet this driving performance, stable electric power should be supplied by a catenary system. Various factors affect the current collection performance, most important of which is the dynamic characteristics of a pantograph. In this paper, the sensitivity analysis and design optimization of a pantograph for a high-speed train were conducted using a finite element method. The dynamic catenarypantograph interaction was analyzed by using the commercial finite element analysis software, SAMCEF. The pantograph was modeled as a three degrees of freedom mass-spring-damper system, and the pre-sag of the contact and messenger wire due to gravity was implemented. The span data of a high-speed line was applied in the analysis model. And the dynamic characteristics of the pantograph model were obtained by a performance test. The reliability of the simulation model was verified by comparing the analysis contact force results with the test data. By simulation, the mean contact force and its standard deviation etc. were evaluated, and then sensitivity of the pantograph was analyzed. Based on the sensitivity analysis results, the specification of the pantograph was optimized. In the optimization process, response surface analysis and differential evolutionary algorithm were applied to define the regressive function and to determine the optimum values for stable current collection performance. Finally, the improvement of the current collection performance was verified by comparing the optimum specification results with the original specification.     

基于实数编码遗传算法的平面度评定

将基于实数编码的遗传算法应用于平面度的评定.根据尺寸和公差的数学定义,建立完全符合最小区域条件的平面度评定的数学模型,并在此基础上给出遗传算法的适应度函数.随后详细地介绍了算法的实现步骤,在基于实数编码的基础上,遗传选择操作采用一种正比选择策略--转轮法,遗传交叉操作采用简单算术交叉法,而遗传变异操作是随机均匀实数变异操作.最后对文献[5]的实验数据进行了评定,仿真结果表明该算法不仅合理,而且效率高、精度高,优于其它算法.     

一种改进遗传算法及其在图像复原中的应用

遗传算法的参数中交叉率和变异率的选择是影响遗传算法行为和性能的关键,直接影响算法的收敛性.论文提出了一种适用于实数编码的改进遗传算法,通过综合交叉和随机变异等手段,避免了人为确定交叉率和变异率,从而使算法更加稳健,在提高搜索效率的同时减少陷入局部最优的机会.研究了该方法在图像复原中的应用,并通过运动模糊图像复原的仿真实验,提供了图像复原的一种新思路.采用本方法恢复模糊图像,不必对点扩散函数作出精确的估计,仿真结果表明该算法能很好地恢复出已退化的图像,显示了改进遗传算法的优点,得到了一种图像复原的新方法.     

混合遗传算法在网络拥塞控制中的仿真研究

随着互联网技术的迅猛发展,网络拥塞日益成为了限制计算机网络发展的一个瓶颈,为了实现拥塞控制,建立了多约束优化数学模型,并提出一种混合遗传算法。该算法将禁忌搜索融入到遗传算法中,既可避免遗传算法陷入局部最优解,也为禁忌搜索提供了较好的初始个体,因此可以加快算法收敛,提高解的质量。仿真结果表明该优化算法减小了端到端的延迟,降低了丢包率,从而避免了网络拥塞、优化了网络资源利用。     

用顺序功能图实现复杂顺序PLC控制

用顺序功能图可以实现复杂顺序PLC控制,具有简单、直观、高效和不易出错等特点,是目前对复杂顺序控制的最好方法。以送料小车的控制为例,用通用指令实现顺序功能图步进的思想,提高编程的效率。