复杂滑带条件下的岩体 参数反演研究

结合1967年稚碧江唐古栋滑坡1 -1剖面模型,综合比较分析了相关岩体各工况下的稳定 性,确定了最危险工况,以极限平衡原理确定了最危险滑面下强度参数,并取其满足规范要求的最低 稳定性标准为反演目标。由于滑带可能穿越不同的岩层,反演过程中需要分别对不同岩层进行危险滑 带搜索,以确定未知岩层的力学参数。结果显示岩体参数反演在工程上具有较高的可靠性,可为实际 边坡工程提供重要的依据。     

基于GLUE 方法的饱和多孔介质中溶质运移模型参数不确定性分析

模型参数的不确定性分析是模型不确定性研究的重要内容之一。本文以示踪剂Br和反应性溶质Cu在石英砂中的运移为例,采用GLUE方法探讨了多孔介质中溶质运移模型参数的不确定性。研究结果表明,仅对水力学参数θs和λ进行识别时,θs和λ的可识别性较强。对耦合Freundlich等温吸附的模型参数进行识别时,由于参数间的相互作用,θs和λ的可识别性降低;吸附特性参数kF的后验分布基本呈均匀分布,可识别性较差,吸附特性参数β、ω、f的取值区间则相对收敛,可识别性较强。K-S检验结果表明,参数区域敏感度由高到底的排序为f、ω、β、kF、λ、θs,主要是因为石英砂对Cu的吸附以动力学反应为主,而f和ω是与动力学吸附反应相关的两个参数。上述结论有助于加深对溶质运移模型参数的理解和提高模型预测的可靠性。     

Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm

Machine loading problem in a flexible manufacturing system (FMS) encompasses various types of flexibility aspects pertaining to part selection and operation assignments. The evolution of flexible manufacturing systems offers great potential for increasing flexibility by ensuring both cost-effectiveness and customized manufacturing at the same time. This paper proposes a linear mathematical programming model with both continuous and zero-one variables for job selection and operation allocation problems in an FMS to maximize profitability and utilization of system. The proposed model assigns operations to different machines considering capacity of machines, batch-sizes, processing time of operations, machine costs, tool requirements, and capacity of tool magazine. A genetic algorithm (GA) is then proposed to solve the formulated problem. Performance of the proposed GA is evaluated based on some benchmark problems adopted from the literature. A statistical test is conducted which implies that the proposed algorithm is robust in finding near-optimal solutions. Comparison of the results with those published in the literature indicates supremacy of the solutions obtained by the proposed algorithm for attempted model.     

Agility and production flow layouts: An analytical decision analysis

The term ‘agile manufacturing’ has referred to operational aspects of a manufacturing company concerning their ability to produce customized products at mass production prices and with short lead times. A core issue faced within agile manufacturing is the need for appropriate and supporting production and operations systems. Many design dimensions of agility and agile manufacturing exist. To help attain this goal for integrating the many design dimensions, operations infrastructure and capacity must be carefully planned to manage production flow, and thus production layout planning takes on an increasingly important role. Given the importance of these dimensions in response to agility, this paper seeks to make a contribution by providing insights into a decision aid for evaluating production flow layouts that support and enhance the agile manufacture of products. Layout design has a significant impact on the performance of a manufacturing or service industry system and has been an active research area for many decades. Strategic evaluation of production layouts requires consideration of both qualitative and quantitative factors (managerial, organizational, and technical). This paper makes use of the Analytical Network Process (ANP) which captures interdependencies among different criteria, sub-criteria and dimensions, an evident characteristic of production flow layouts in complex agile manufacturing environments. An application case study exemplifying the practical usefulness of this type of model describes how management, after implementation of the model, made a mid-course correction related to the production layout initially selected.     

Modified alternating direction-implicit iteration method for linear systems from the incompressible Navier–Stokes equations

In order to solve the large sparse systems of linear equations arising from numerical solutions of two-dimensional steady incompressible viscous flow problems in primitive variable formulation, Ran and Yuan [On modified block SSOR iteration methods for linear systems from steady incompressible viscous flow problems, Appl. Math. Comput. 217 (2010), pp. 3050–3068] presented the block symmetric successive over-relaxation (BSSOR) and the modified BSSOR iteration methods based on the special structures of the coefficient matrices. In this study, we present the modified alternating direction-implicit (MADI) iteration method for solving the linear systems. Under suitable conditions, we establish convergence theorems for the MADI iteration method. In addition, the optimal parameter involved in the MADI iteration method is estimated in detail. Numerical experiments show that the MADI iteration method is a feasible and effective iterative solver.     

Hybrid shape parameter strategy for the RBF approximation of vibrating systems

Shape parameters play an important role in radial basis function (RBF) approximations. Therefore, the choice of them is an active field in numerical analysis research. In this paper, first we review the available strategies in the literature for selecting shape parameters. Then, we introduce an alternative approach called hybrid strategy for scaling the RBFs. This strategy is constructed based on the advantages of the older ones and discards their disadvantages. The efficiency of the new strategy is demonstrated by comparing the effects of different strategies on approximating the eigenvalues of ordinary and partial differential equations with different boundary conditions.     

Optimum parameter for the SOR-like method for augmented systems

Recently, several proposals for the generalization of Young's SOR method to the saddle point problem or the augmented system has been presented. One of the most practical versions is the SOR-like method given by Golub et al., [(2001). SOR-like methods for augmented systems. BIT, 41, 71–85.], where the convergence and the determination of its optimum parameters were given. In this article, a full characterization of the spectral radius of the SOR-like iteration matrix is given, and an explicit expression for the optimum parameter is given in each case. The new results also lead to different results to that of Golub et al. Besides, it is shown that by the choices of the preconditioning matrix, the optimum SOR-like iteration matrix has no complex eigenvalues, therefore, it can be accelerated by semi-iterative methods.     

Workflow simulation for operational decision support using event graph through process mining

It is increasingly common to see computer-based simulation being used as a vehicle to model and analyze business processes in relation to process management and improvement. While there are a number of business process management (BPM) and business process simulation (BPS) methodologies, approaches and tools available, it is more desirable to have a systemic BPS approach for operational decision support, from constructing process models based on historical data to simulating processes for typical and common problems. In this paper, we have proposed a generic approach of BPS for operational decision support which includes business processes modeling and workflow simulation with the models generated. Processes are modeled with event graphs through process mining from workflow logs that have integrated comprehensive information about the control-flow, data and resource aspects of a business process. A case study of a credit card application is presented to illustrate the steps involved in constructing an event graph. The evaluation detail is also given in terms of precision, generalization and robustness. Based on the event graph model constructed, we simulate the process under different scenarios and analyze the simulation logs for three generic problems in the case study: 1) suitable resource allocation plan for different case arrival rates; 2) teamwork performance under different case arrival rates; and 3) evaluation and prediction for personal performances. Our experimental results show that the proposed approach is able to model business processes using event graphs and simulate the processes for common operational decision support which collectively play an important role in process management and improvement.     

From fine-grained to abstract process models: A semantic approach

Organizations actively managing their business processes face a rapid growth of the number of process models that they maintain. Business process model abstraction has proven to be an effective means to generate readable, high-level views on business process models by showing coarse-grained activities and leaving out irrelevant details. In this way, abstraction facilitates a more efficient management of process models, as a single model can provide for many relevant views. Yet, it is an open question how to perform abstraction in the same skillful way as experienced modelers combine activities into more abstract tasks. This paper presents an approach that uses semantic information of a process model to decide on which activities belong together, which extends beyond existing approaches that merely exploit model structural characteristics. The contribution of this paper is twofold: we propose a novel activity aggregation method and suggest how to discover the activity aggregation habits of human modelers. In an experimental validation, we use an industrial process model repository to compare the developed activity aggregation method with actual modeling decisions, and observe a strong correlation between the two. The presented work is expected to contribute to the development of modeling support for the effective process model abstraction.     

Process diagnostics using trace alignment: Opportunities, issues, and challenges

Business processes leave trails in a variety of data sources (e.g., audit trails, databases, and transaction logs). Hence, every process instance can be described by a trace, i.e., a sequence of events. Process mining techniques are able to extract knowledge from such traces and provide a welcome extension to the repertoire of business process analysis techniques. Recently, process mining techniques have been adopted in various commercial BPM systems (e.g., BPM|one, Futura Reflect, ARIS PPM, Fujitsu Interstage, Businesscape, Iontas PDF, and QPR PA). Unfortunately, traditional process discovery algorithms have problems dealing with less structured processes. The resulting models are difficult to comprehend or even misleading. Therefore, we propose a new approach based on trace alignment. The goal is to align traces in such a way that event logs can be explored easily. Trace alignment can be used to explore the process in the early stages of analysis and to answer specific questions in later stages of analysis. Hence, it complements existing process mining techniques focusing on discovery and conformance checking. The proposed techniques have been implemented as plugins in the ProM framework. We report the results of trace alignment on one synthetic and two real-life event logs, and show that trace alignment has significant promise in process diagnostic efforts.