Safety Sampling: A Case Study

Safety sampling is a method by which management studies people and processes with the aim of making a safer workplace. This is accomplished by identifying, detecting, and reducing the unsafe acts people and processes exhibit prior to accidents. The purpose of this article is to present a case study of safety sampling.     

Robust Design using Pareto type optimization: A genetic algorithm with arithmetic crossover

In dual response systems (DRSs) optimization restrictions on the secondary response may rule out better conditions, since an acceptable value for the secondary response is usually unknown. In fact, process conditions that result in a smaller standard deviation are often preferable. Recently, several authors stated that the standard deviation of any performance property could be treated as a new property in its own right as far as Pareto optimizer was concerned. By doing this, there will be many alternative solutions (i.e., the trade-offs between the mean and standard deviation responses) of the DRS problem and Pareto optimization can explore them all. Such analysis is useful, and that is required in order to achieve an improved understanding of the problem before searching for a final optimal solution. In this paper, we again follow this new philosophy and solve the DRS problem by using a genetic algorithm with arithmetic crossover. The genetic algorithm is applied to the printing process problem for improving the quality of a printing process. Genetic algorithms, in contrast to the one-solution-at-a-time approach of most optimization algorithms, maintain a population of hundreds, or thousands, of solutions in speedy manner.     

成熟探区未发现油藏规模预测

利用油藏发现过程的不放回不等概率抽样的特点，在假设油藏总体服从巴内托分布的前提下，可以根据已发现油藏规模和发现时间的信息，采用抽样分析理论预测油藏总体。位于东营凹陷南斜坡的王家岗-八面河成藏体系勘探近40年，属于成熟探区，用该方法预测其油藏规模总体和未发现油藏的规模，结果表明，王家岗-八面河探区规模大于50储量单位的油藏资源总量为42000储量单位，未发现的17000储量单位多分布于规模小于500储量单位的油藏中。图2表1参9     

On the Pareto optimum sensitivity analysis in multicriteria optimization

To analyse the trade‐off relations among the set of criteria in multicriteria optimization, Pareto optimum sensitivity analysis is systematically studied in this paper. Original contributions cover two parts: theoretical demonstrations are firstly made to validate the gradient projection method in Pareto optimum sensitivity analysis. It is shown that the projected gradient direction evaluated at a given Pareto optimum in the design variable space rigorously corresponds to the tangent direction of the Pareto curve/surface at that point in the objective space. This statement holds even for the change of the set of active constraints in the perturbed problem. Secondly, a new active constraint updating strategy is proposed, which permits the identification of the active constraint set change, to determine the influence of this change upon the differentiability of the Pareto curve and finally to compute directional derivatives in non‐differentiable cases. This work will highlight some basic issues in multicriteria optimization. Some numerical problems are solved to illustrate these novelties. Copyright © 2003 John Wiley & Sons, Ltd.     

Multicriteria optimization of a high yield pulping process with rough sets

The optimization of complex processes usually involves many competing objectives; in this case there is typically no solution that yields optimal values for all of the objective criteria and the decision-maker must therefore find a reasonable compromise. In recent years, new multicriteria methods have been developed to assist the practitioner in achieving a judicious compromise among the various competing objectives. One method, the rough set method (RSM), is able to encapsulate the preferences of an expert within a simple set of logical rules that are used to rank a large number of feasible solutions according to these preferences.The RSM was used in this investigation to determine the optimal operating region of a high yield pulping process. This pulping process has seven input variables that can be manipulated to optimize four objective criteria characterizing the product: brightness, specific refining energy, extractive content, and rupture length.Results show that an optimal solution zone can be easily defined and zones of decreasing preference can be drawn. This information is very useful to the practitioner for choosing the desired operating conditions and for analyzing the robustness of the preferred operating scenarios from a process control point of view.     

基于改进MOBPSO算法的含分布式电源的多目标配电网重构

提出一种含多类分布式电源的多目标配电网重构模型。在重构时采用Pareto准则的多目标二进制粒子群优化(Multi-Objective Binary Particle Swarm Optimization, MOBPSO)算法,对算法进行了3点改进：基于环的编码方式用以降低不可行解的产生概率;动态变化的惯性因子和异步变化的学习因子用以提高算法的调节适应能力;在最优粒子的更新方式上提出小生境共享机制和比例选择算子相结合的策略。最后还给出了一种根据决策者偏好信息,从优化解集中选择相应重构方案的评价机制。算例结果表明：改进的MOBPSO算法在寻优效率和稳定性上均有提升;所提模型、改进算法以及评价机制互相配合,能够为决策者提供一种有效的配电网重构方案。     

基于工艺参数扰动的IC参数成品率多目标优化算法

在芯片制造工艺中,参数扰动影响了集成电路（Integrated Circuit,IC）成品率,使不同参数成品率间存在着此消彼长的相互制约关系,而目前IC参数成品率优化算法却主要局限于单一优化目标问题。本文提出一种基于工艺参数扰动的参数成品率多目标优化算法。该算法针对漏电功耗成品率及芯片时延成品率,首先构建具有随机相关性的漏电功耗及芯片时延统计模型;随后根据其相互制约特性建立基于切比雪夫仿射理论的参数成品率多目标优化模型;最后利用自适应加权求和得到分布均匀的帕雷托优化解。实验结果表明,该算法对于具有不同测试单元的实验电路均可求得大约30个分布均匀的帕雷托优化解,不仅能够有效权衡多个优化目标间的相互制约关系,还可以使传统加权求和优化方法在帕雷托曲线变化率较小之处得到优化解。     

基于改进Pareto最优算法的海上风电场多目标微观选址规划

为了在海上风电场初步规划时可以兼顾考虑风机尾流影响、集电系统的经济性和可靠性,用风机层数表征集电系统可靠性,以风电场尾流折减系数、海缆投资、风机层数为优化目标,建立海上风电场多目标微观选址规划模型,并通过改进Pareto多目标优化算法求解最优解集,最后应用层次分析法进行最终优化决策。以某海上风电场的风资源分布状况为实例,验证了风机尾流、连接海缆及风机层数之间的相互制约关系,在规划时应予以综合考虑,同时证明了模型和方法的可行性,改进Pareto最优算法可以求出均匀分布的备选方案,层次分析法可以根据不同要求选择出对应的最优方案。     

Multi‐variate time‐series simulation

In this article we present a method for simulating a multi‐variate time series via a vector auto regressive moving average (p, q) process. We also carried out two simulation studies to check the performance of the method and applied the methodology to a real sea condition time series. All results show that the method works very well in practice.