A goal-programming enhanced collaborative optimization approach to reducing lifecyle costs for naval vessels

Understanding the trade-offs involved in assessing lifetime cost for engineering systems requires understanding trends in various engineering disciplines that require significantly different analysis methods to efficiently explore. The corresponding design spaces can be flat, defined by weak minima, and thus difficult to understand using traditixonal optimization methods. This paper presents a new multi-disciplinary framework that uses a goal-programming enhanced multi-objective collaborative optimization (eMOCO) approach to facilitate the development of the spaces. In order to further increase its efficiency in discrete or flat spaces well-suited to evolutionary optimization a unique discipline level genetic algorithm is proposed. Naval vessels are an example of an engineering system that has a difficult design space with respect to lifetime cost, however, one where it is critical to understand. As these costs are increasing, they are becoming limiting factors in a vessel’s operational life. Though they are so important, the interaction between different cost categories such as production and operation has not been explored in depth and is not always clear. Understanding the trade-offs between different aspects of a vessel’s total ownership costs early in the design stage can aid in the production of new ships where they are minimized. The proposed framework is verified on mathematical problems, and then used to develop trade-spaces between resistance and production for a nominal naval combatant vessel. These trade-spaces show both the knowledge gained by designers in understanding these trade-offs and the ability of the proposed eMOCO framework to develop them effectively.     

Assembling off-the-shelf components: "Learn as you Go" systems engineering

The process of developing new information systems has evolved from custom software development to assembly of off-the-shelf components. The change has significantly reduced both the costs and time to develop new capabilities, and as a notable result, e-business systems have been implemented at a very rapid pace. An assembly sequence (components to be assembled, corresponding dates and costs) has several risks including: 1) technical risk: successful (or not) function of assembled components by planned schedule milestones; 2) operational risk: achieving (or not) the desired business value by using the new system of assembled components; and 3) programmatic (schedule and cost) risks: accomplishing the assembly within time and budget constraints. As assembly proceeds, estimates of technical performance and operational value at the time of system completion can be adjusted, and one should consider what early milestones of component assembly suggest about later milestones. The technical community can be both hesitant to reveal and ascertain the results of combining off-the-shelf products into a working system, and it is typical to have significant cost and schedule overruns due to technical problems that are discovered late in system assembly. The operational community can be surprised by the results achieved in applying new capabilities, causing significant changes to what was originally desired from a new system. This paper presents a framework for planning and adjusting milestone sequences in assembling off-the-shelf software components. The framework balances technical and operational risks within established cost and time constraints.     

Shape Optimization by Free-Form Deformation: Existence Results and Numerical Solution for Stokes Flows

Shape optimization problems governed by PDEs result from many applications in computational fluid dynamics. These problems usually entail very large computational costs and require also a suitable approach for representing and deforming efficiently the shape of the underlying geometry, as well as for computing the shape gradient of the cost functional to be minimized. Several approaches based on the displacement of a set of control points have been developed in the last decades, such as the so-called free-form deformations. In this paper we present a new theoretical result which allows to recast free-form deformations into the general class of perturbation of identity maps, and to guarantee the compactness of the set of admissible shapes. Moreover, we address both a general optimization framework based on the continuous shape gradient and a numerical procedure for solving efficiently three-dimensional optimal design problems. This framework is applied to the optimal design of immersed bodies in Stokes flows, for which we consider the numerical solution of a benchmark case study from literature.     

The implementation of a knowledge-based framework for the aerodynamic optimization of a morphing wing device

In the field of aerospace engineering currently a lot of research effort is directed towards the reduction of cruise drag of civil transport aircraft in order to reduce fuel burn, and hence environmental impact and costs. In order to reduce cruise drag, a promising method is under consideration by adjusting, or rather morphing the rear part of the aircraft’s wing during cruise flight. Given the premature state of knowledge of such a design implementation, a knowledge-based computational framework is developed. The purpose of this framework is to allow for an aerodynamic optimization of a section of the wing. The framework is set up in such a way that all relevant design knowledge generated in the process can be captured and used in a subsequent mechanical design process. In this fashion, the complex design process of a novel morphing wing device can be automated to a certain degree. This automation can be used to construct a large number of different feasible and optimized designs with varying boundary conditions of a complex experimental device.This article describes the initial 2-dimensional aerodynamic design step of the morphing device under consideration and how it is implemented in a knowledge-based optimization framework. It describes the initial stage of the development of this tool, as it will be expanded by a number of design steps that each adds more detail to the design in all relevant aspect fields (aerodynamic, structural, actuation, etc.). Ultimately, this tool will be used to obtain a thorough evaluation of a number of different proposed structural solutions and allow for a comparison between them.     

On the computational complexity of combinatorial flexibility problems

The concept of flexibility – originated in the context of heat exchanger networks design – is associated with a substructure which allows the same optimal value on the substructure (for example an optimal flow) as in the whole structure, for all the costs in a given range of costs. In this work, we extend the concept of flexibility to general combinatorial optimization problems, and prove several computational complexity results in this new framework. Under some monotonicity conditions, we prove that a combinatorial optimization problem can be polynomially reduced to its associated flexibility problem. However, the minimum cut, maximum weighted matching and shortest path problems have NP-complete associated flexibility problems. In order to obtain polynomial flexibility problems, we have to restrict ourselves to combinatorial optimization problems on matroids.     

Numerical Propulsion System Simulation

The cost of implementing new technology in aerospace propulsion systems is becoming prohibitively expensive. One of the major contributors to the high cost is the need to perform many large scale system tests. Extensive testing is used to capture the complex interactions among the multiple disciplines and the multiple components inherent in complex systems. The objective of the Numerical Propulsion System Simulation (NPSS) is to provide insight into these complex interactions through computational simulations. This will allow for comprehensive evaluation of new concepts early in the design phase before a commitment to hardware is made. It will also allow for rapid assessment of field-related problems, particularly in cases where operational problems were encountered during conditions that would be difficult to simulate experimentally. The tremendous progress taking place in computational engineering and the rapid increase in computing power expected through parallel processing make this concept feasible within the near future. However it is critical that the framework for such simulations be put in place now to serve as a focal point for the continued developments in computational engineering and computing hardware and software. The NPSS concept which is described below will provide that framework.     

面向J2EE框架的代码自动生成引擎设计及实现

针对目前J2EE应用系统项目开发存在交付滞后、维护性差等问题,提出了一种新的软件自动生成方法。设计了代码自动生成引擎的总体框架,给出了各个组成部分的详细设计方案,对关键组件进行了具体实现。该引擎为提高软件开发效率和降低软件开发成本提供了一种行之有效的途径和方法,在一定程度上降低了软件开发的风险。在实际项目开发中验证了该系统的可行性。     

Generalized Discrete Software Reliability Modeling With Effect of Program Size

Generalized methods for software reliability growth modeling have been proposed so far. But, most of them are on continuous-time software reliability growth modeling. Many discrete software reliability growth models (SRGM) have been proposed to describe a software reliability growth process depending on discrete testing time such as the number of days (or weeks); the number of executed test cases. In this paper, we discuss generalized discrete software reliability growth modeling in which the software failure-occurrence times follow a discrete probability distribution. Our generalized discrete SRGMs enable us to assess software reliability in consideration of the effect of the program size, which is one of the influential factors related to the software reliability growth process. Specifically, we develop discrete SRGMs in which the software failure-occurrence times follow geometric and discrete Rayleigh distributions, respectively. Moreover, we derive software reliability assessment measures based on a unified framework for discrete software reliability growth modeling. Additionally, we also discuss optimal software release problems based on our generalized discrete software reliability growth modeling. Finally, we show numerical examples of software reliability assessment by using actual fault-counting data     

A generic framework for multi-criteria decision support in eco-friendly urban logistics systems

In this paper, we present a multi-objective shortest path evolutionary algorithm for comprehensive solutions to real-world manifestations of the classical vehicle routing problem. The shift from being a purely academic pursuit is highlighted by the introduction of a generic optimization framework which accommodates a variety of attributes that commonly occur in industrial applications. Specifically, the paper's main contribution are as follows: (1) consideration for the following real-world constraints: (a) time windows at customer locations, (b) simultaneous pickup and delivery demands, (c) a heterogeneous fleet of vehicles, and (d) the heterogeneity of traffic congestion levels in urban transportation networks; (2) assimilation of all the above attributes into a multi-objective program which aims to minimize environmental impact, while simultaneously addressing the overall operational costs of the routing solution and service quality concerns; a feat that has not been fully realized by known intelligent systems according to the authors’ best knowledge. In order to showcase the efficacy of the proposed algorithm, it is first tested on existing benchmark instances and then applied on a pair of real-world industrial examples from Singapore. These industrial examples serve as a source of new benchmarks which facilitate the study of different routing constraints and their effects on the economic and environmental viability of urban logistics systems.     

Designing IT systems according to environmental settings: A strategic analysis framework

In the past, IT system design is mainly driven by two essential factors: technical merits and costs. Environmental consideration only emerges in most recent discussions under the label of green IT. As the evaluation of environmental and climate impact involves too many parameters, some of which are indirect and hidden, it is very hard to make rationale analysis without the support of a holistic strategic analysis framework. In this paper, we propose to extend the goal-oriented requirements modeling language, GRL, to model the rationality behind IT system design, in particular, how the environment related considerations come into play in such design decision making. It can be adopted as a strategic analysis framework to facilitate concrete decision makings under different environmental settings. Example real world scenarios are used to illustrate how the proposed approach can help improve the state of the practice.     

An Object-Oriented Framework for Multidisciplinary, Multi-Physics, Computational Mechanics

This paper presents the design and development of an object-oriented framework for computational mechanics. The framework has been designed to address some of the major deficiencies in existing computational mechanics software packages. The framework addresses the deficiencies of existing computational mechanics software packages by (a) having a sound design using the state of the art in software engineering, and (b) providing model manipulation features that are common to a large set of computational mechanics problems. The framework provides features that are essential to a large set of computational mechanics problems. The domainspecific features provided by the framework are a geometry sub-system specifically designed for computational mechanics, an interpreted Computational Mechanics Language (CML), a structure for management of analysis projects, a comprehensive data model, model development, model query and analysis management. The domain independent features provided by the framework are a drawing subsystem for data visualization, a database server, a quantity subsystem, a simple GUI and an online help server. It is demonstrated that the framework can be used to develop applications that can: (a) extend or modify important parts of the framework to suit their own needs; (b) use CML for rapid prototyping and extending the functionality of the framework; (c) significantly ease the task of conducting parametric studies; (d) significantly ease the task of modeling evolutionary problems; (e) be easily interfaced with existing analysis programs; and (f) be used to carry out basic computational mechanics research. It is hoped that the framework will substantially ease the task of creating families of software applications that apply existing and upcoming theories of computational mechanics to solve both academic and real world interdisciplinary simulation problems.     

A framework for the ex-ante evaluation of ERP software

It has been widely reported that a large number of ERP implementations fail to meet expectations. This is indicative, firstly, of the magnitude of the problems involved in ERP systems implementation and, secondly, of the importance of the ex-ante evaluation and selection process of ERP software. This paper argues that ERP evaluation should extend its scope beyond operational improvements arising from the ERP software/product per se to the strategic impact of ERP on the competitive position of the organisation. Due to the complexity of ERP software, the intangible nature of both costs and benefits, which evolve over time, and the organisational, technological and behavioural impact of ERP, a broad perspective of the ERP systems evaluation process is needed. The evaluation has to be both quantitative and qualitative and requires an estimation of the perceived costs and benefits throughout the life-cycle of ERP systems. The paper concludes by providing a framework of the key issues involved in the selection process of ERP software and the associated costs and benefits.     

Design of a framework for modeling, integration and simulation of physiological models

Multiscale modeling and integration of physiological models carry challenges due to the complex nature of physiological processes. High coupling within and among scales present a significant challenge in constructing and integrating multiscale physiological models. In order to deal with such challenges in a systematic way, there is a significant need for an information technology framework together with related analytical and computational tools that will facilitate integration of models and simulations of complex biological systems. Physiological Model Simulation, Integration and Modeling Framework (Phy-SIM) is an information technology framework providing the tools to facilitate development, integration and simulation of integrated models of human physiology. Phy-SIM brings software level solutions to the challenges raised by the complex nature of physiological systems. The aim of Phy-SIM, and this paper is to lay some foundation with the new approaches such as information flow and modular representation of the physiological models. The ultimate goal is to enhance the development of both the models and the integration approaches of multiscale physiological processes and thus this paper focuses on the design approaches that would achieve such a goal.     

Combining analytic kernel models for energy-efficient data modeling and classification

Energy-efficient computing has now become a key challenge not only for data-center operations, but also for many other energy-driven systems, with the focus on reducing of all energy-related costs, and operational expenses, as well as its corresponding and environmental impacts. However, current intelligent data models are typically performance driven. For instance, most data-driven machine-learning approaches are often known to require high computational cost in order to find the global optima. Designing more accurate intelligent data models to satisfy the market needs will hence lead to a higher likelihood of energy waste due to the increased computational cost. This paper thus introduces an energy-efficient framework for large-scale data modeling and classification/prediction. It can achieve a predictive accuracy comparable to or better than the state-of-the-art machine-learning models, while at the same time, maintaining a low computational cost when dealing with large-scale data. The effectiveness of the proposed approaches has been demonstrated by our experiments with two large-scale KDD data sets: Mtv-1 and Mtv-2.     

基于CHAM的软件连接件形式化模型

软件连接件是软件体系结构中的一个重要概念，是构件间相互作用的媒体。用形式化方法对其模型加以描述是软件体系结构研究中的一个重点。化学抽象机形式化语言(CHAM：Chemical Abstract Machine)是一种通用的、简单直观的计算模型描述语言。本文在分析了连接件语义模型的基础上，提出了一种基于CHAM的软件连接件描述模型，从软件连接件静态组成及动态行为两个方面对连接件进行形式化、规范化地描述和分析，以期进一步实现简化软件连接件的设计、增强其通用性的目的。     

考虑碳排放的物流网络优化模型研究

考虑碳排放的物流网络优化问题, 分两阶段建立考虑碳排放的配送中心选择与需求匹配模型和碳排放成本最小的配送路径优化模型, 通过两阶段模型的配合使用, 解决了物流网络中配送中心选址、不同物流节点间的需求匹配以及配送车辆路径优化等多种决策问题。对“考虑碳排放”与“不考虑碳排放”情况进行Lingo软件的数值分析, 结果表明：考虑碳排放的物流网络综合物流成本明显降低, 比不考虑碳排放的传统方法更具现实意义。     

Including stakeholder input in formulating and solving real-world optimisation problems: Generic framework and case study

Multi-objective evolutionary algorithms (MOEAs) are becoming increasingly popular for solving formal environmental and water resources optimisation problems. In the past, the focus of these studies has generally been on methodological issues related to the optimisation algorithm. However, in recent years, there has been increased recognition of the need to apply these approaches to real-world problems to facilitate the realisation of their full potential. In order to assist with this, a framework for including stakeholder input in real-world optimisation problems is introduced in this paper, including a conceptual framework and a procedure for implementing it. The framework is applied to an urban water supply security study for Adelaide, South Australia. This study highlights the role of stakeholder input at the various stages of the optimisation process, as well as the resulting changes in the formulation, analysis and results. A discussion of the lessons learnt from the case study is also provided.     

基于混合蚁群算法的异质车队低碳VRP研究

针对货运车辆在配送调度过程中产生大量碳排放的问题，建立模型将多种影响碳排放量的因素协同优化。模型中考虑了不同载重量的异质车队，两个节点之间有多条道路的柔性路径，以及车辆重量随卸货而减少的动态负载等因素，以碳排放量、行驶时间和行驶路程为优化目标，并加入了节点需求时间窗、根据速度变化划分路段、交接和卸货时间的约束。提出了一种混合蚁群算法，利用蚁群算法信息素强度更新方式保持群体记忆性，利用粒子群算法的快速收敛特性增加计算效率。通过随机数值算例的仿真优化与对比分析，验证了算法和模型的有效性。     

Fully probabilistic control for uncertain nonlinear stochastic systems

This paper develops a novel probabilistic framework for stochastic nonlinear and uncertain control problems. The proposed framework exploits the Kullback–Leibler divergence to measure the divergence between the distribution of the closed-loop behavior of a dynamical system and a predefined ideal distribution. To facilitate the derivation of the analytic solution of the randomized controllers for nonlinear systems, transformation methods are applied such that the dynamics of the controlled system becomes affine in the state and control input. Additionally, knowledge of uncertainty is taken into consideration in the derivation of the randomized controller. The derived analytic solution of the randomized controller is shown to be obtained from a generalized state-dependent Riccati solution that takes into consideration the state- and control-dependent functional uncertainty of the controlled system. The proposed framework is demonstrated on an inverted pendulum on a cart problem, and the results are obtained.     

分布式电源接入场景下的电网振荡攻击建模与检测

随着分布式电源在电网中所占比重的不断提升,针对分布式电源的攻击将给电网带来更严重的安全威胁。攻击者可以通过网络入侵手段协同控制电网中防御较弱的配网侧分布式电源功率输出,最终影响发电侧发电机等关键设备的安全运行。为保障电网安全稳定运行,亟需研究针对分布式电源接入场景下的安全威胁及其防御措施。首先,本文在电力系统动态模型基础之上建立了电网振荡攻击的最小代价攻击模型,通过协同控制多个分布式电源的功率,在牺牲最少被控节点的前提下导致电网发生振荡。其次,针对现有振荡检测算法的不足,本文提出一种启发式的攻击源检测算法,通过分析系统内各节点的势能变化,可有效辅助定位攻击源。算例仿真分析结果验证了通过最小代价攻击影响电网稳定运行的可行性,以及攻击检测方法的有效性。