USE OF COUNTERPROPAGATION NEURAL NETWORKS TO ENHANCE THE CONCURRENT SUBSPACE OPTIMIZATION STRATEGY

The paper explores the use of artificial neural networks in a concurrent optimization strategy that derives from a decomposition based approach to design of large-scale engineering systems. These problems are characterized by complex couplings that render parametric design methods inappropriate as solution tools. Decomposition methods reduce the large dimensionality problem into a sequence of smaller, more tractable optimization problems, each with a smaller set of design variables and constraints. The decomposed subproblems are rarely decoupled completely, and design changes in one subproblem have a profound influence on changes in another subproblem. Essential components of decomposition based design methods are strategies to identify a topology for problem decomposition, and to develop coordination strategies which account for couplings among the decomposed problems. The paper examines the effectiveness of artificial neural networks as a tool to both account for the coupling, and to develop methods to coordinate the solution in the different subproblems to a converged optimal design     

A self-adaptive fe system based on recursive spatial decomposition and multigrid analysis

This paper introduces an automatic self-adaptive finite element system for 3-D stress analysis of homogeneous elastic solids described in a Solid Modelling System (SMS). This system reduces the role of the analyst into two basic tasks: defining the problem (i.e. geometry, material properties and boundary conditions) and specifying the desired accuracy in the analysis. The approach followed in this work is based on Recursive Spatial Decomposition (RSD) of solids defined in a SMS. The system includes procedures for meshing, analysis, error estimation, and adaptive remeshing and reanalysis. The various procedures share the same hierarchical data structure built on the RSD.     

Graph partitioning strategy for the topology design of industrial network

Network topology design problem in industrial network is formulated, which is shown to be equivalent to a multi-constraint optimisation problem: the network design should minimise the amount of inter-network communication, and simultaneously balance the communication load and network size evenly over the resultant sub-networks. To solve this optimisation problem, a graph partitioning strategy is proposed, which can give a good network design by partitioning a graph- based representation of the network optimisation problem. Then, the network optimisation procedures using the graph partitioning strategy are detailed and two experimental, examples are studied. In the experiments, the network designs obtained by the graph partitioning strategy are compared with those obtained by a random partitioning method. The experimental results demonstrate the network designs obtained by the graph partitioning strategy are significantly better than those obtained by the random partitioning method.     

A boundary element strategy for elastostatic inverse problems involving uncertain boundary conditions

In this paper, a convenient strategy is developed to find solutions for a class of uncertain‐boundary‐value problems by the Boundary Element Method (BEM). Such problems are ill‐posed, but ill‐conditioning of the associated algebraic systems of equations can be controlled to a large extent, and useful boundary data can be obtained despite ill‐conditioning. Interior data of not only sufficient quantity, but also of good quality at good locations contribute to good solutions. Our strategy permits the condition number of the algebraic systems, as a function of interior‐data locations, to be monitored, such that measured data from displacement sensors and/or strain sensors, at locations found to be good ones for the problem at hand, can be used. The present strategy is based upon the concept of a Green's‐function library through partitioning of the BEM algebraic system. Algebraic systems are solved using least squares via Singular Value Decomposition (SVD). The library idea takes advantage of modern data storage and retrieval technology and permits the process of repeated trials, in order to determine good data sensor locations, to be done quickly and efficiently. Several numerical examples are given to demonstrate the strategy. Some examples examine the consequences of errors in measured data. Copyright © 1999 John Wiley & Sons, Ltd.     

Multilevel Green's function interpolation method for scattering from composite metallic and dielectric objects

A multilevel Green's function interpolation method based on two kinds of multilevel partitioning schemes--the quasi-2D and the hybrid partitioning scheme--is proposed for analyzing electromagnetic scattering from objects comprising both conducting and dielectric parts. The problem is formulated using the surface integral equation for homogeneous dielectric and conducting bodies. A quasi-2D multilevel partitioning scheme is devised to improve the efficiency of the Green's function interpolation. In contrast to previous multilevel partitioning schemes, noncubic groups are introduced to discretize the whole EM structure in this quasi-2D multilevel partitioning scheme. Based on the detailed analysis of the dimension of the group in this partitioning scheme, a hybrid quasi-2D/3D multilevel partitioning scheme is proposed to effectively handle objects with fine local structures. Selection criteria for some key parameters relating to the interpolation technique are given. The proposed algorithm is ideal for the solution of problems involving objects such as missiles, microstrip antenna arrays, photonic bandgap structures, etc. Numerical examples are presented to show that CPU time is between O(N) and O(N log N) while the computer memory requirement is O(N).     

A multi-mode failure boundary exploration and exploitation framework using adaptive kriging model for system reliability assessment

Various adaptive reliability analysis methods based on surrogate models have recently been developed. A multi-mode failure boundary exploration and exploitation framework (MFBEEF) was proposed for system reliability assessment using the adaptive kriging model based on sample space partitioning to reduce computational cost and use the characteristics of the failure boundary in multiple failure mode systems. The efficiency of the adaptive construction of kriging model can be improved by using the characteristics of the center sample of the small space to represent the characteristics of all samples in the small space. This method proposes a failure boundary exploration and exploitation strategy and a convergence criterion based on the maximum failure probability error for a system with multiple failure modes to adaptively approximate the failure boundary of a system with multiple failure modes. A multiple-failure-mode learning function was used to identify the optimal training sample to gradually update the kriging model during the failure boundary exploration and exploitation stages. In addition, a complex failure boundary-oriented adaptive hybrid importance sampling method was developed to improve the applicability of the MFBEEF method to small failure probability assessments. Finally, the MFBEEF method was proven to be effective using five system reliability analysis examples: a series system, a parallel system, a series–parallel hybrid system, a multi-dimensional series system with multiple failure modes, and an engineering problem with multiple implicit performance functions.     

模具企业生产调度管理系统的研究

为适应模具企业新的生产方式,本文提出了一种结合合同网协议、GPGP协同机制和TAEMS任务描述语言的任务分配和调度方法,并建立层/级结构的整体框架和各级调度策略.该方法从整体的角度分析问题,提高了调度结果的一致性和优化性.初步的验证表明该方法是有效的.     

Dynamic-demand capacitated facility location problems with and without relocation

We consider two multi-period dynamic-demand capacitated location problems. In the first problem, the facilities are allowed to be relocated in each period, whereas in the second they are kept at a fixed location determined at the beginning of the planning horizon. We provide Lagrangian Relaxation and Benders Decomposition algorithms, including an ?-optimal BD algorithm, for the solution for the first model and a Benders Decomposition algorithm for the second. For detailed analysis, we generate a wide variety of instances to test the performance of the algorithms by taking into account varying number of customer locations and time periods in the planning horizon as well as fixed cost structures and facility capacities. We observe that the efficiency of the solution algorithms depends on the input data structure, specifically the cost structures, the facility capacities (which, in turn, dictate the expected number of open facilities), and the variation in the total customer demand from period to period.     

A matrix-based clustering approach for the decomposition of design problems

In matrix-based modeling, a design problem is represented by a rectangular matrix that captures the dependency relationships between design functions and parameters. To decompose such a matrix-based design problem, the two-phase method, which adapts hierarchical cluster analysis, has been proposed in literature. Yet, the clustering strategy in general is still challenging since the decomposition of design problems requires simultaneous clustering of two types of objects (i.e., design functions and parameters). In this paper, the notion of coupling is advanced by characterizing the coupling between two objects of different types. Accordingly, the two-phase method is revised via two methodical components: coupling-based dependency analysis and tree-based partitioning analysis. While the coupling-based dependency analysis concatenates different types of coupling for organizing the dependency structure, the tree-based partitioning analysis utilizes the coupling information from dependency analysis to identify design sub-problems and their interactions. Due to a better understanding of the coupling notion, the revised approach is able to simplify the algorithmic procedure and narrow down the solution search space without compromising the solution quality. Two examples (i.e., a relief valve system and a powertrain system) are used to demonstrate and justify the utility of the revised method.     

Parametric modeling of an electromagnetic compression device with the proper generalized decomposition

Optimization of forming processes seeks an optimal choice of many process parameters. In Electromagnetic Material Forming (EMF), parameters associated to the geometry of the forming device or related to the generation of the pulsed currents have to be set, and are of primary importance to achieve the proper geometry of the formed part. Usual optimization procedures proceed by defining a trial choice of the set of parameters and then evaluate the optimality of a given cost function computed from a direct analysis. This iterative process requires many assessments of the cost function and may lead to a prohibitive computation cost since the direct analysis may involve a structural analysis. Others approaches have been proposed to circumvent this problem; based on a separated representation of the solution, the Proper Generalized Decomposition allows for a parametric resolution by introducing optimization parameters as extra-coordinates of the problem, hence the optimization procedure reduces to a simple post-treatment of the multidimensional numerical solution. The aim of this work is to develop a numerical tool dedicated to the optimization of the design of an electromagnetic compression device. This tool should enable to optimize process parameters of the generator and geometrical parameters of the electromagnetic forming device by solving the set of electromagnetic equations in quasistatics. To this end, we take advantage of the Proper Generalized Decomposition (PGD) to perform a parametric resolution. We show solutions computed with a parameterization of the discharged current, and with a parameterization of the geometry considering a multi-layered structure. Finally, an example of optimization procedure is shown on the latter solution, seeking the configuration maximising the radial component of the resultant compression force applied on the part to be formed.     

基于商业信用和收益共享的再制造闭环供应链协调模型

研究了在商业信用条件下闭环供应链的协调问题。利用Stackelberg 主从博弈方法分析了分散决策模式下制造商和零售商的定价策略,接着分析了集中决策模式下的定价策略,提出了一个基于商业信用和收益共享的闭环供应链协调机制,通过算例分析讨论了各参数对决策结果的影响,定量刻画了商业信用在闭环供应链协调中的价值。研究表明商业信用和收益共享契约能有效地协调闭环供应链,对闭环供应链管理实践具有较大的指导作用。     

基于AHP的深基坑支撑梁爆破拆除顺序研究

深基坑支撑梁爆破拆除施工顺序的确定属于一个多因素决策分析问题,以AHP分析方法为基础对影响深基坑支撑梁爆破拆除施工顺序的因素进行分类和构建层次结构模型,并在分析过程中结合数值计算的方法改进了判断矩阵的确定方法,使得改进后的判断矩阵更具有客观性,提高了决策的可靠性。结合武汉某深基坑支撑梁爆破拆除项目,对其进行分区,通过Abaqus及AHP分析方法进行爆破拆除模拟计算,并在实际施工中验证了结论的正确性和可实施性。     

基于虚拟储能的综合能源系统分布式电源功率波动平抑策略

针对含分布式可再生能源的热电联供综合能源系统,提出一种基于虚拟储能技术的热电联供综合能源系统联络线功率波动平抑策略。分析了综合能源系统中热电联供系统的结构特点与电热耦合方式。热电联供综合能源系统中的可控设备包括微型燃气轮机、热泵、蓄电池和超级电容。虚拟储能系统是基于建筑物蓄热特性的模型。引入可控设备及虚拟储能系统状态指标,采用加权滑动平均滤波算法确定联络线目标平滑功率;将联络线波动功率在可控设备间分配,并基于设备状态映射表,引入设备修正系数以确定其最终出力。在上海某高校"中意绿色能源实验中心"进行验证,结果表明,所提策略能够实现电热耦合协调,保证功率波动平抑效果,在满足虚拟储能状态约束的前提下,保证客户的舒适度,延长可控设备的使用寿命,并充分发掘热电联供综合能源系统的控制灵活性,增强分布式可再生能源的综合消纳能力。     

A Distributed Intrusion Detection Model via Nondestructive Partitioning and Balanced Allocation for Big Data

There are two key issues in distributed intrusion detection system, that is, maintaining load balance of system and protecting data integrity. To address these issues, this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation. A data allocation strategy based on capacity and workload is introduced to achieve local load balance, and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster. Moreover, data integrity is protected by using session reassemble and session partitioning. The simulation results show that the new model enjoys favorable advantages such as good load balance, higher detection rate and detection efficiency.     

An adaptive model order reduction with Quasi‐Newton method for nonlinear dynamical problems

Model Order Reduction (MOR) methods are extremely useful to reduce processing time, even nowadays, when parallel processing is possible in any personal computer. This work describes a method that combines Proper Orthogonal Decomposition (POD) and Ritz vectors to achieve an efficient Galerkin projection, which changes during nonlinear solving (online analysis). It is supported by a new adaptive strategy, which analyzes the error and the convergence rate for nonlinear dynamical problems. This model order reduction is assisted by a secant formulation which is updated by the Broyden‐Fletcher‐Goldfarb‐Shanno (BFGS) formula to accelerate convergence in the reduced space, and a tangent formulation when correction of the reduced space is needed. Furthermore, this research shows that this adaptive strategy permits correction of the reduced model at low cost and small error. Copyright © 2015 John Wiley & Sons, Ltd.     

A variable-depth search algorithm for the recursive bipartitioning of signal flow graphs

We discuss the use of local search techniques for mapping video algorithms onto programmable high-performance video signal processors. The mapping problem is very complex due to many constraints that need to be satisfied in order to obtain a feasible solution. The complexity is reduced by decomposing the mapping problem into three subproblems, namely delay management, partitioning, and scheduling. We present the partitioning problem and the representation of video algorithms by signal flow graphs. Furthermore, we propose a solution strategy that is based on recursive bipartitioning of these graphs. The bipartitions are generated using a variable-depth search algorithm. The results demonstrate that the frequently cited flexibility of local search techniques can be successfully exploited in handling complicated problems.     

Dynamic optimization of structures with non-linearities

Dynamic optimal design of structural systems with local non-linearities is proposed and the corresponding mathematical programming is established in this paper. A predictor-corrector method is presented for state and sensitivity analysis of the systems which is based on partitioning and interface prediction of linear and non-linear regions. The non-linear programming problem is linearized and solved by a coarse search SLP approach. A model of an automatic weapon is optimized and some interesting results are given to illustrate the effectiveness of the present method.     

A bilevel game theoretic approach to optimum design of flywheels

Multiobjective optimization problems arise frequently in mechanical design. One approach to solving these types of problems is to use a game theoretic formulation. This article illustrates the application of a bilevel, leader–follower model for solving an optimum design problem. In particular, the optimization problem is modelled as a Stackelberg game. The partitioning of variables between the leader and follower problem is discussed and a variable partitioning metric is introduced to compare various variable partitions. A computational procedure based on variable updating using sensitivity information is developed for exchanging information between the follower and leader problems. The proposed approach is illustrated through the design of a flywheel. The two objective functions used for the design problem include maximizing the kinetic energy stored in the flywheel while simultaneously minimizing the manufacturing cost.     

Production lead-time hedging and coordination in prefabricated construction supply chain management

This paper studies a coordination scheme to solve a production lead-time hedging (PLTH) issue in the prefabricated construction supply chain management. To mitigate tardiness delivery owing to prefab production uncertainty, the project contractor (PC) prefers to inform the prefab factory (PF) an earlier due date which is termed as PLTH strategy. However, this strategy forces the PF to compress its production process. A balance must be optimised through the proposed PLTH coordination scheme. It includes a PLTH-related cost term, i.e. crashing money (charged by the PF to the PC) and a constant transfer term. Three models with different power structures are considered. Firstly, two Stackelberg games with alternative decision-making sequences and an equal power model are discussed to obtain the optimal PLTH amount and crashing money. Later on, a cost-sharing term is used to fairly allocate the system surplus. It is observed that this coordination scheme reduces the PLTH amount and ensures win–win coordination for both parties. Some interesting managerial implications are also obtained from comparison analysis and numerical studies.     

Multi-period hedging and coordination in a prefabricated construction supply chain

In this paper, a multi-period buffer space hedging coordination between a building contractor (BC) and a logistics provider (LP) is investigated. In order to facilitate the application of the construction lead-time hedging (CLTH) strategy, adopted by the BC, extra buffer space needs to be reserved at LP’s intermediate warehouse for contingency usage. This strategy is defined as ‘buffer space hedging (BSH)’ and it increases the pressure of LP on involving extra storage and maintenance cost. Two coordination mechanisms are adopted for solving this BSH problem. One scheme is by introducing a cost-sharing term. A Nash game model is studied to find the individual optimal decisions. Another scheme is a cooperative game model with proper side-payments. We show that adjusting the BSH amount for each review period benefits the supply chain. Both coordination mechanisms enable a win–win outcome. Especially, if system resources are sufficient, the cooperative game outperforms the Nash game. Numerical experiments further demonstrate that the benefit of the proposed model is more significant under the case with lower unit enlarging/rearrangement cost, higher tardiness penalty and unpredictable and uncontrollable construction process-determined assembled and installed prefabs.