An intelligent adaptive system for the optimal variable selections of R&D and quality supply chains

The cost of research & development (R&D) and quality management are always regarded as two major parts of total cost. The variable performance of R&D and quality design is an important index that will reflect the effectiveness of the cost reduction. This research has attempted to simultaneously vary all of the variables to achieve the global optimum for the optimal variable selections of R&D and quality design. Genetic algorithm (GA) can treat all of the variables for the global search. In this study, fuzzy refinement with orthogonal arrays was effective in improving the performance of the GA, and also showed the benefits of a good chromosome structure on the behavior of GA. It is also proposed the postponement design with temporal concept, to select the effective variables for the cost reduction of R&D and quality management design. The experimental results showed that tempo-postponement design will increase the flexibility and quick response for supply chain management. Hence, this approach can act as a useful guideline for researchers working on the optimization of the key variable selections for R&D and quality model design.     

Targets and results from phase one and two of the fifth generation computer systems study

This article presents an introduction to the fifth generation computer systems.

Fifth generation computers are under development to meet the knowledge information processing needs for the advanced information-oriented society of the 1990s. This clearly requires overcoming the technical limitations of conventional computers. A fifth generation computer requires problem solving and inference functions for inference of solutions to problems from accumulated knowledge, knowledge base management functions for acquirement and retrieval of knowledge, intelligent interface functions for fluent conversations using natural language, drawings, images, etc., and intelligent programming functions for automatic conversion of problem specifications submitted in natural language, drawing etc., into efficient programs.

Fifth generation computers with these functions will have far-reaching impact finding applications in all areas of industry and society. However, research and development (R&D) of these computers requires an extremely wide variety of leading-edge technology, and the risks are high. Accordingly, a long-term R&D plan was established for this project that extends over ten years with the initial stage lasting from 1982 to 1984, the intermediate stage from 1985 to 1988, and the final stage will begin in 1989 and the project will end in 1991.     

R&D project evaluation model based on fuzzy set priority

Most of the conventional concepts used by the R&D project evaluation models do not seem to be appropriate for modeling the imprecision R&D project evaluation. This paper is concerned with the project evaluation by aggregating the multiple rank-ordered sets based on fuzzy set priority. First the rank-ordered priority lists of the R&D projects were determined based on the majority-rule methods, then the aggregate fuzzy set rank order was computed and compared with the others. Finally, we have developed a systemic and practical program suitable for a simple and easy calculation of all the algorithms. It was found that our model was validated by the comparative computations in various R&D project examples.     

A decision support system for inventory management in high research & development environment

Inventory management is being discussed recently as one of the key elements for survival and success in the production industry today. Little efforts were made to solve inventory problems in the Hi R&D environment.

In this paper, a prototype of a Microcomputer Based Decision Support is introduced to support decisions for managing Hi R&D inventories. The decisions involve timing and scheduling of purchased components, as well as classification of those items.     

计算预知未来——基于国家高性能计算环境的生物医药应用服务社区

基于国家高性能计算环境的生物医药应用服务社区得到了国家十三五重点研发专项的继续支持,通过项目实施将进一步推进高性能计算在生物信息和药物研发领域的应用服务。项目将从生物医药应用服务社区、精准医疗和个性化药物应用示范两个课题开展社区建设和应用服务推广的研究,为患者量身设计出最佳治疗方案,以期达到疗效最大化和副作用最小化;为医院临床研究提供可能的更精准更个性化的医疗解决方案,产生经济和社会效益。     

A NEW APPROACH TO FILTERING AND ADAPTIVE CONTROL: OPTIMALITY RESULTS

Abstract

In a previous paper (Tesfatsion, in press) a new method was proposed for adaptive control. The key distinguishing feature is the direct consistent estimation and updating of the criterion (expected returns) function without recourse to prior state space specification, updated state probabilities, and Bayes's rule. The stability of a simple linear criterion function filtering scheme designed for control-dependent states was investigated in detail. In particular, it was shown that control variable sequences selected in accordance with the directly updated criterion function estimates converge under plausible restrictions to a local maximum of the true criterion function.

The principal purpose of the present paper is to establish sufficient conditions for control variables selected in accordance with the linear scheme to converge to a global maximum of the true criterion function. As will be clarified, when states are nontrivially dependent on control variable selection, the decision maker determines the trade-off between rate of convergence and asymptotic global optimality of control variable selections by his choice of greatest lower bound for the prior (initial period) criterion function. For trivial state-control dependence the asymptotic global optimality of control variable selections holds under weak restrictions.     

Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures

In this paper, we present a generic method/model for multi-objective design optimization of laminated composite components, based on Vector Evaluated Artificial Bee Colony (VEABC) algorithm. VEABC is a parallel vector evaluated type, swarm intelligence multi-objective variant of the Artificial Bee Colony algorithm (ABC). In the current work a modified version of VEABC algorithm for discrete variables has been developed and implemented successfully for the multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria: failure mechanism based failure criteria, maximum stress failure criteria and the tsai-wu failure criteria. The optimization method is validated for a number of different loading configurations—uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences, as well fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. Finally the performance is evaluated in comparison with other nature inspired techniques which includes Particle Swarm Optimization (PSO), Artificial Immune System (AIS) and Genetic Algorithm (GA). The performance of ABC is at par with that of PSO, AIS and GA for all the loading configurations.     

关联变量分组的分解多目标进化算法研究

含有大规模决策变量的优化问题是当前多目标进化算法领域中的研究热点和难点之一。在解决大规模变量问题时,目前的进化算法并没有寻找决策变量之间的关联信息,而都只是将所有变量视为一个整体来进行优化。但随着优化问题中决策变量的增多,“变量维度”成为瓶颈,从而影响算法的性能。针对上述问题,提出关联变量分组策略,通过识别决策变量间内在的关联信息把关联变量分配到同组中,将复杂高维变量的优化问题分解为简单低维的子问题来求解。该策略通过增加关联变量分配到同组中的概率来使算法尽可能地保留变量之间的关联性,减少分组后子问题间的依赖性,从而提高子问题最优解的质量并最终获得最佳的Pareto最优解集。将该算法在标准测试函数上进行变量扩展后再进行仿真对比实验,采用性能指标对算法的收敛性和多样性进行对比分析。实验结果表明,该算法在解决大规模变量的多目标优化问题中,随着决策变量维度的增加,比经典的多目标进化算法NSGA-II、MOEA/D以及RVEA具有更佳的收敛和更好的分布性能,所求得的Pareto解集质量更高。     

Multidisciplinary Design Optimization Framework for the Pre Design Stage

Presented is a novel framework for performing flexible computational design studies at preliminary design stage. It incorporates a workflow management device (WMD) and a number of advanced numerical treatments, including multi-objective optimization, sensitivity analysis and uncertainty management with emphasis on design robustness. The WMD enables the designer to build, understand, manipulate and share complex processes and studies. Results obtained after applying the WMD on various test cases, showed a significant reduction of the iterations required for the convergence of the computational system. The tests results also demonstrated the capabilities of the advanced treatments as follows:

The novel procedure for global multi-objective optimization has the unique ability to generate well-distributed Pareto points on both local and global Pareto fronts simultaneously.

The global sensitivity analysis procedure is able to identify input variables whose range of variation does not have significant effect on the objectives and constraints. It was demonstrated that fixing such variables can greatly reduce the computational time while retaining a satisfactory quality of the resulting Pareto front.

The novel derivative-free method for uncertainty propagation, which was proposed for enabling multi-objective robust optimization, delivers a higher accuracy compared to the one based on function linearization, without altering significantly the cost of the single optimization step.

The work demonstrated for the first time that such capabilities can be used in a coordinated way to enhance the efficiency of the computational process and the effectiveness of the decision making at preliminary design stage.     

Progress on standardizing OSI conformance testing

International standards for Open Systems Interconnection (OSI) services and protocols are well advanced. Complementary standardization work has begun for testing conformance of products to OSI protocol standards. This is drawing on about 7 years R&D work on techniques and tools for testing protocol implementations. This paper presents the major aspects of the testing methodology and framework being standardized by ISO and CCITT. It relates some of the ideas to the work done in the R&D community and identifies the main topics on which further work is needed.     

Truss optimization on shape and sizing with frequency constraints based on parallel genetic algorithm

Truss shape and sizing optimization under frequency constraints is extremely useful when improving the dynamic performance of structures. However, coupling of two different types of design variables, nodal coordinates and cross-sectional areas, often lead to slow convergence or even divergence. Because shape and sizing variables coupled increase the number of design variables and the changes of shape and sizing variables are of widely different orders of magnitude. Otherwise, multiple frequency constraints often cause difficult dynamic sensitivity analysis. Thus optimal criteria and mathematical programming methods have considerable limitations on solving the problems because of needing complex dynamic sensitivity analysis and being easily trapped into the local optima. Genetic Algorithms (GAs) show great potentials to solve the truss shape and sizing optimization problems. Since GAs adopt global probabilistic population search techniques and require no gradient information. The improved genetic algorithms can effectively increase the solution quality. However, the serial GA is computationally expensive and is limited on gaining higher quality solutions. To solve the truss shape and sizing optimization problems with frequency constraints more effectively and efficiently, a Niche Hybrid Parallel Genetic Algorithm (NHPGA) is proposed to significantly reduce the computational cost and to further improve solution quality. The NHPGA is to blend the advantages of parallel computing, simplex search and genetic algorithm with niche technique. Several typical truss optimization examples demonstrate that NHPGA can significantly reduce computing time and attain higher quality solutions. It also suggests that the NHPGA provide a potential algorithm architecture, which effectively combines the robust and global search characteristics of genetic algorithm, strong exploitation ability of simplex search and computational speedup property of parallel computing.     

2D动画项目实施过程的工程化思想初探

低成本、高质量的动画作品是动漫企业共同追求的目标。文章将工程化思想在2D动画项目的实施过程加以利用。探索性的建立一套关于2D动画项目的实施过程模型、管理体系和理论指导,为2D动画项目的有效实施提供参考。     

Evaluating the user-centredness of development organisations: conclusions and implications from empirical usability capability maturity assessments

Improving the position and effectiveness of user-centred design, UCD, in software and product development is a challenge in many companies. One step towards improvements is to carry out a usability capability maturity, UCM, assessment to evaluate the strengths and weaknesses of a development organisation in user-centred design. This article reports the lessons learnt from 11 empirical UCM assessments of R&D groups of Nokia, a software house, an SME, and a research institute in Finland. The first assessments were carried out using a standard process assessment model (a pre-version of ISO 18529); the last assessments were carried out using a new KESSU model that evolved during the research. It was found that the assessment model, its interpretation, and the viewpoints of the assessment team have a critical role in the success of assessments. In addition, it was found that the customers have different purposes for assessments and those purposes have an effect on how one should conduct the assessment—or whether to conduct it at all.     

Application of SEUMRE global optimization algorithm in automotive magnetorheological brake design

In this paper, the superior performance of a novel space exploration and unimodal region elimination global optimization algorithm, SEUMRE, is demonstrated through comparisons with other well known global optimization techniques, including genetic algorithm (GA), simulated annealing (SA), and a highly nonlinear design problem—the optimal design of automotive magnetorheological brake (MRB). Unlike the conventional brakes, an MRB employs the interaction between a magnetorheological fluid and an applied magnetic field to generate the retarding braking torque. The SEUMRE design optimization algorithm was used to maximize the braking torque and minimize the weight of the brake structure. The computation time and optimized design parameters illustrated SEUMRE’s capability to converge to an accurate result faster than the conventional global optimization methods. However, SA provided significantly better optimization results than GA and SEUMRE in terms of the cost function.     

OSI implementations in Korea

Research and development on OSI became a key issue in many Korean R&D projects related to computer, telecommunications and services. The Korean computer and communications industry also finds their possible area of expansion through OSI-compatible products. But claiming OSI as the future direction in Korea at the national/government level is not near. This short paper presents various present and future R&D projects on OSI in Korea. These include ISDN, LAN, Videotex, Teletex, protocol testing, mixed mode terminal, network architecture etc.     

A computer-based model for system level reliability and maintainability allocation

This paper describes computer-based heuristic models for Reliability and Maintainability (R&M) allocation for large systems. The model is an embellishment to the Maintenance Allocation Program (MAP) developed at McDonnell Douglas Corporation. The new version of MAP known as REMAP (Reliability Embellished MAP) is a decision support tool for contractors who are involved in large scale design projects such as aircraft design. The REMAP allows the user to give preliminary R&M information about the system. The REMAP gives “what if” changes on the requirements required to meet the desired design reliability specification.     

A genetic algorithm to refine input data selection for air temperature prediction using artificial neural networks

The accurate prediction of air temperature is important in many areas of decision-making including agricultural management, transportation and energy management. Previous research has focused on the development of artificial neural network (ANN) models to predict air temperature from one to twelve hours in advance. The inputs to these models included a constant duration of prior data with a fixed resolution for all environmental variables for all prediction horizons. The overall goal of this research was to develop more accurate ANN models that could predict air temperature for each prediction horizon. The specific objective was to determine if the ANN model accuracy could be improved by applying a genetic algorithm (GA) for each prediction horizon to determine the preferred duration and resolution of input prior data for each environmental variable. The ANN models created based on this GA based approach provided smaller errors than the models created based on the existing constant duration and fixed data resolution approach for all twelve prediction horizons. Except for a few cases, the GA generally included a longer duration for prior air temperature data and shorter durations for other environmental variables. The mean absolute errors (MAEs) for the evaluation input patterns of the one-, four-, eight-, and twelve-hour prediction models that were based on this GA approach were 0.564 °C, 1.264 °C, 1.766 °C and 2.018 °C, respectively. These MAEs were improvements of 3.98%, 4.59%, 2.55% and 1.70% compared to the models that were created based on the existing approach for the same corresponding prediction horizons. Thus, the GA based approach to determine the duration and resolution of prior input data resulted in more accurate ANN models than the existing ones for air temperature prediction. Future work could examine the effects of various GA and fitness evaluation parameters that were part of the approach used in this study.     

A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation

In this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimise the number of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as simulated annealing and variable neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, genetic algorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a controlled migration operator into the GA heuristic, data, which represents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-the-loop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits.     

A genetic-based methodology for hybrid electric vehicles sizing

 As private transport concerns, the global challenge of this millennium is the reduction of carbon dioxide emissions from passenger cars by improving fuel economy without sacrificing the vehicle performance. Hybrid electric vehicles powertrain, combining electric motor with an auxiliary power unit, can improve effectively the vehicle performance and fuel economy, reducing at the same time the effects of the use of private cars on the air quality of the cities. These advantages can be achieved only if the design of the powertrain is inspired to the minimisation of the main figures of merit holding in consideration many general aspects and variables. As supporting methodology in developing this difficult activity, a genetic-based sizing methodology will be presented. It will be aimed to minimise a function objective which takes into account not only technical specifications but also environmental, social, and economic aspects. Some interesting simulation results will be reported to prove the validity of the methodology, which will contribute to a substantial reduction of the pollutant emissions from hybrid electric vehicles.