Optimal parameters design of oilfield surface pipeline systems using fuzzy models

Establishing and operating an oilfield surface pipeline system involves a large capital expenditure, which includes pipeline cost, power consumption cost, and thermal energy cost, etc. In this process, there exists a vast amount of fuzzy information, in both the objective and the constraint functions for an optimal design. System design based on such a huge volume of fuzzy information about multi-objectives and multi-constraints cannot be well handled by conventional mathematical tools. In this paper, a mathematical model from the fuzzy optimization approach is formulated for such oilfield surface pipeline systems. Both the multi-objective and multi-constraint functions are defined in a feasible fuzzy domain within the design parameters space. An optimal solution is selected from this domain for the design, for which the degree of expert satisfaction reaches the maximum. The methodology presented in this paper offers a new approach and a significant improvement of cost savings in the establishment and operation of an oilfield surface pipeline network system, beneficial for the oil industry.     

Towards embedded model predictive control for System-on-a-Chip applications

We propose a framework for embedding model predictive control for Systems-on-a-Chip applications. In order to allow the implementation of such a computationally expensive controller on chip, we propose reducing the precision of the microprocessor to the minimum while maintaining near optimal control performance. Taking advantage of the low precision, a logarithmic number system based microprocessor architecture is used, that allows the design of a reduced size processor, providing further energy and computational cost savings. The design parameters for this high-performance embedded controller are chosen using a combination of finite element method simulations and bit-accurate hardware emulations in a number of parametric tests. We provide the methodology for choosing the design parameters for two particular control problems; the temperature regulation in a wafer cross-section geometry, and the control of temperature in a non-isothermal fluid flow problem in a microdevice. Finally, we provide the microprocessor architecture details and estimates for the performance of the resulting embedded model predictive controller.     

Reticle Design for Minimizing Multiproject Wafer Production Cost

Multiproject wafer (MPW) production cost is sensitive to how the chips are arranged in a reticle. In this paper, we propose a methodology for exploring the reticle floorplan design space to minimize MPW production cost. Experimental results show that our methodology often achieves double-digit cost savings. A study using MPW for volume production shows that the volume cutoff points range from a few thousand dice to tens of thousands of dice. Note to Practitioners-This paper proposes a methodology for minimizing MPW production cost via better chip placement in a reticle (called reticle floorplanning). Our methodology consists of an effective reticle floorplanning method, two simulated wafer dicing methods, two cost estimation models, and a procedure for calculating the cost assumed by each project. A design service company or a foundry can use our methodology to reduce MPW production cost and, thus, provides a more affordable and expedient service to its customers. The reticle floorplanning method and simulated wafer dicing methods employed here are the state-of-the-art. A practitioner should adapt these methods to other MPW problems such as dealing with multitechnology process, placing multiple instances of the same design in a reticle, etc. The cost models should also be revised accordingly. The cost data given in this paper should be used only for reference as mask tooling and wafer fabrication costs constantly change. The cost model proposed for calculating the production cost assumed by each project can serve as a basis for developing a fairer pricing model. The study of using MPW for low to medium-volume production is also very useful. It may help a customer deliver its product earlier to market using a low-cost fabrication program. The problem addressed in this paper becomes much simpler if the side-to-side wafer dicing constraint is removed.     

A Data-Driven Methodology for Evaluating and Optimizing Call Center IVRs

Usability of many call center IVRs (Interactive Voice Response systems) is dismal. Callers dislike touch-tone IVRs and seek agent assistance at the first opportunity. However, because of high agent costs, call center managers continue to seek automation with IVRs. The challenge for call centers is providing user-friendly, yet cost-efficient, customer service. This article describes a comprehensive methodology for usability re-engineering of telephone voice user interfaces based on detailed call center assessment and call flow redesign. At the core of our methodology is a data-driven IVR assessment, in which we analyze end-to-end recordings of thousands of calls to evaluate IVR cost effectiveness and usability. Because agent time is the major cost driver in call center operations, we quantify cost-effectiveness in terms of agent time saved by automation in the IVR. We identify usability problems by carefully inspecting user-path diagrams, a visual representation of the sequence of events of thousands of calls as they flow through the IVR. Such an IVR assessment leads directly into call-flow redesign. Assessment insights lead to specific suggestions on how to improve a call-flow design. In addition, the assessment enables us to estimate the cost savings of a new design, thus providing the necessary business justification. We illustrate our IVR usability and re-engineering methodology with examples from large commercial call centers, demonstrating how the staged process maximizes the payback for the call center while minimizing risk.     

An integrated methodology for manufacturing systems design using manual and computer simulation

Traditional approaches to manufacturing systems design utilize a sequential procedure that focuses on production capacity requirements, with human operator task design developed late in the systems design phase. Implementing manufacturing systems in this way is difficult when operations management must design flexible and efficient processes, with an often incomplete understanding of how people can best perform within the system. This study developed an integrated methodology that uses both manual and computer simulations to evaluate system performance and ergonomic issues early in the system design process. Information about operator performance and ergonomics is obtained in the manual simulations, while estimates of operator utilization and system throughput is obtained through computer simulations. An iterative design process is used, with the results of manual and computer simulations informing each other during subsequent simulations. An industrial case study is presented here to demonstrate the effectiveness of this methodology. The results show that the methodology can be used to design manufacturing systems with significant savings in labor cost and improved manufacturing system flexibility. © 2003 Wiley Periodicals, Inc. Hum Factors Man 13: 19–40, 2003.     

双网通信模式测控系统的研究与实现

提出一种具有Wi-Fi/GPRS双网模式通信功能的测控系统设计。该系统的网络传输通道既可以是Wi-Fi网络,也可以是GPRS网络,双网模式解决测控设备快速接网时,布线复杂、成本高和可靠性差等问题,为远程通信事业提供便利节约成本。手机监控终端是Android系统智能手机,如果手机使用者有用户的监视密码,则可以监视用户现场的所有模块的实时数据和初始化参数。如果手机使用者有用户的控制密码则可以修改用户现场的参数,实现远方校准,修改模块的工作模式。     

Incorporating uncertainty in optimal decision making: Integrating mixed integer programming and simulation to solve combinatorial problems

We introduce a novel methodology that integrates optimization and simulation techniques to obtain estimated global optimal solutions to combinatorial problems with uncertainty such as those of facility location, facility layout, and scheduling. We develop a generalized mixed integer programming (MIP) formulation that allows iterative interaction with a simulation model by taking into account the impact of uncertainty on the objective function value of previous solutions. Our approach is generalized, efficient, incorporates the impact of uncertainty of system parameters on performance and can easily be incorporated into a variety of applications. For illustration, we apply this new solution methodology to the NP-hard multi-period multi-product facility location problem (MPP-FLP). Our results show that, for this problem, our iterative procedure yields up to 9.4% improvement in facility location-related costs over deterministic optimization and that these cost savings increase as the variability in demand and supply uncertainty are increased.     

A multi-point reduced-order modeling approach of transient structural dynamics with application to robust design optimization

Predicting the transient response of structures by high-fidelity simulation models within design optimization and uncertainty quantification often leads to unacceptable computational cost. This paper presents a reduced-order modeling (ROM) framework for approximating the transient response of linear elastic structures over a range of design and random parameters. The full-order response is projected onto a lower-dimensional basis spanned by modes computed from a proper orthogonal decomposition (POD) of full-order model simulation results at multiple calibration points. The basis is further enriched by gradients of the POD modes with respect to the design/random parameters. A truncation strategy is proposed to compensate for the increase in basis vectors due to the proposed enrichment strategies. The accuracy, efficiency and robustness of the proposed framework are studied with a two-dimensional model problem. The numerical results suggest that the proposed ROM approach is well suited for large parameter changes and that the number of basis vectors needs to be increased only linearly with the number of design and random parameters to maintain a particular ROM performance. The application of the proposed ROM approach to robust shape optimization demonstrates significant savings in computational cost over using full-order models. Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy’s National Nuclear Security Administration under Contract DE-AC04-94AL85000.     

Integrated product and manufacturing system platforms supporting the design of personalized medicines

Pharmaceutical product customization, a prerequisite for personalized medicines, is currently a widely researched topic. Patient characteristics can be mapped and translated into parameters for designing patients’ individual treatment, i.e., the dosage form. However, current pharmaceutical manufacturing is dominated by mass production and lacks the capability and flexibility required to produce customized products. Mass customization is a proven successful approach in, for example, the manufacturing industry and thus has been discussed as an enabler for pharmaceutical product customization but has never been fully explored in a pharmaceutical context. Inspired by mass customization approaches in the manufacturing industry, this study proposes a novel methodology to develop integrated product and manufacturing system platforms for pharmaceutical products supporting a mass customization paradigm. The proposed methodology establishes sets of product and manufacturing system platform variants and suggests an approach to feasible platform design selection. The applicability of the proposed methodology is illustrated for diabetes treatment as a selected case example. Integrated platform designs are developed for the conventional treatment of a fully integral tablet design and for a design enabling product customization with a modularized tablet design. The manufacturing platforms are still embracing a mass production design in the methodology illustration and should elicit knowledge on the utility of the current production design in a mass customization context. The performance and utility of the respective platform are assessed in terms of production cost and patient benefit. The results suggest a substantial increase in patient benefit afforded by the modularized tablet design, however the production cost is increased. This trade-off between the production cost and patient benefit thus calls for novel manufacturing system concepts to achieve the feasible manufacturing of customized pharmaceutical products.     

Optimum cost design of welded box beams with longitudinal stiffeners using advanced backtrack method

The use of longitudinal stiffeners in box girders loaded in bending results in savings in weight and cost. To study these savings the optimized box beams without and with stiffeners are compared to each other. The minimum cross-sectional area design can be solved analytically. A cost function is defined containing material and fabrication (welding) costs. This function is nonlinear in the structural dimensions to be optimized, therefore an advanced backtrack method is worked out and applied. An illustrative numerical example shows the savings. Received June 30, 1999     

Evolving structural design solutions using an implicit redundant Genetic Algorithm

Performing synthesis during conceptual design provides substantial cost savings by selecting an efficient design topology and geometry, in addition to selecting the structural member properties. A new evolutionary-based representation, which combines redundancy and implicit fitness constraints, is introduced to represent and search for design solutions in an unstructured, multi-objective structural frame problem. The implicit redundant representation genetic algorithm, in tandem with the unstructured problem domain definition, allows the evaluation of diverse frame topologies and geometries. The IRR GA allows the representation of a variable number of location independent parameters, which overcomes the fixed parameter limitations of standard GAs. The novel frame designs evolved by the IRR GA synthesis design method compare favourably with traditional frame design solutions calculated by trial and error. Received May 27, 1999     

Structural optimization of multiple attributes

This paper presents a methodology for structural design optimization of multiple objectives, or attributes. The method represents an improvement over Pareto optimizationbased methods by quantitatively representing trade-offs between conflicting objectives in a single multi-attribute objective function. Classical utility analysis is first used to determine a multi-attribute evaluation function for a particular structure from the designer's viewpoint. This viewpoint takes into account the attribute tradeoffs that are appropriate for a specific project. Since attributes are controlled only indirectly through specification of design decision variables, a new objective function is then formulated which expresses design utility directly in terms of those parameters over which the designer has direct control. A one-bay, three storey steel frame building example demonstrates the methodology for determining the design configuration with the best combination of cost and drift index.     

Server consolidation using performance modeling

Server consolidation is crucial to improving a corporation's bottom line by providing savings in hardware, software, maintenance, facility, and labor costs. In today's cost-cutting environment, consolidation efforts can provide significant savings in IT expenditures. Overall potential consolidation benefits include: lower total cost of ownership, improved service levels and availability, and reduced business risks, resulting from consistent business management and resiliency. To fully realize consolidation benefits, businesses must focus on the performance and service levels provided to users in addition to reducing unused capacity. We present a new way to analyze consolidation alternatives using performance modeling and stepwise refinement, a methodology for efficient and effective modeling.     

An alternative multiple attribute decision making methodology for solving optimal facility layout design selection problems

In the present work, a systematic and an alternative multiple attribute decision making methodology is presented for selection of facility layout design selection problems. The proposed methodology is based on Preference selection index (PSI) method. In the proposed methodology appropriate facility layout design is selected for a given application without considering relative importance between facility layout design selection attributes. Two different types of facility layout design selection problems are examined to demonstrate, validate, and to check the reliability of proposed methodology. In addition, subjective cost benefit analysis is performed to study the benefits to cost to the company. Finally, the study has concluded that the facility layout design selection methodology based on PSI method is simple, logical, and more appropriate for solving the facility layout design selection problems.     

Hybrid morphological methodology for software development cost estimation

In this paper we propose a hybrid methodology to design morphological-rank-linear (MRL) perceptrons in the problem of software development cost estimation (SDCE). In this methodology, we use a modified genetic algorithm (MGA) to optimize the parameters of the MRL perceptron, as well as to select an optimal input feature subset of the used databases, aiming at a higher accuracy level for SDCE problems. Besides, for each individual of MGA, a gradient steepest descent method is used to further improve the MRL perceptron parameters supplied by MGA. Finally, we conduct an experimental analysis with the proposed methodology using six well-known benchmark databases of software projects, where two relevant performance metrics and a fitness function are used to assess the performance of the proposed methodology, which is compared to classical machine learning models presented in the literature.     

Waste-water treatment: Design and optimization using nonlinear search techniques

This study is an attempt to develop an optimization technique for selecting the most cost effective design for the activated sludge waste-water treatment process. The economic model established for accomplishing this task uses an equivalent uniform annual cost expression as a basis for comparison of treatment designs. A modified Hooke and Jeeves search technique is used to determine the treatment design which results in the lowest cost. Substantial savings may be found from the use of the model as compared to the present operations.     

Minimum cost design of a welded orthogonally stiffened cylindrical shell

In this study the optimal design of a cylindrical orthogonally stiffened shell member of an offshore fixed platform truss, loaded by axial compression and external pressure, is investigated. Ring stiffeners of welded box section and stringers of halved rolled I-section are used. The design variables considered in the optimization are the shell thickness as well as the dimensions and numbers of stiffeners. The design constraints relate to the shell, panel ring and panel stringer buckling, as well as manufacturing limitations. The cost function includes the cost of material, forming of plate elements into cylindrical shape, welding and painting. In the optimization a number of relatively new mathematical optimization methods (leap-frog - LFOPC, Dynamic-Q, ETOPC, and particle swarm - PSO) are used, in order to ensure confidence that the finally computed optimum design is accurately determined, and indeed corresponds to a global minimum. The continuous optimization procedures are adapted to allow for discrete values of the design variables to be used in the final manufacturing of the truss member. A comparison of the computed optimum costs of the stiffened and un-stiffened assemblies, shows that significant cost savings can be achieved by orthogonal stiffening, since the latter allows for considerable reduction of the shell thickness, which results in large material and manufacturing cost savings.     

Linear quadratic Nash game-based tracker for multiparameter singularly perturbed sampled-data systems: digital redesign approach

In this paper, a linear quadratic Nash game-based tracker for multiparameter singularly perturbed sample-data systems is developed. A generalized cross-coupled multiparameter algebraic Riccati equation (GCMARE) with two quadratic cost functions is solved by applying the LQR design methodology for the optimal tracker design. Firstly, the asymptotic expansions of the GCMARE are newly established, and the proposed algorithm is able to effectively solve the GCMARE with the quadratic convergence rate. Then, the low-gain digital controller with a high design performance is realized through the prediction-based digital redesign method. Finally, for further improving the tracking performance, the chaos-evolutionary-programming algorithm (CEPA) is utilized to optimally tune the parameters of the tracker. An example is presented to demonstrate the effectiveness of the proposed methodology.     

基于模糊重用库的容错软件开发

容错设计是改进软件可靠性的有效途径，然而由于设计多样性的实现很困难且开销大，导致该项技术目前仅应用于一些安全关键系统之中，为此，提出了一种将重用技术引入到容错软件开发过程中的应用框架，框架以模糊重用库为核心，增强的可靠性开发模型为基础，参数化可靠性指标为相异性设计的依据，简化了容错软件的开发过程，在方法和实践上改进了软件系统的可靠性，介绍了重用库结构，增强的可靠性开发方法，基于重用的相异性设计及其容错软件开发的支持。     

A hybrid intelligent approach for optimizing the fine-pitch copper wire bonding process with multiple quality characteristics in IC assembly

Gold is the primary material used for wire bonding in integrated circuit (IC) assembly. Owing to the high appreciation in the price of gold, copper (Cu) wire has become an important substitute material in order to save on manufacturing costs. However, an average of 40% in yield loss during IC assembly can be attributed to improper control of the Cu wire bonding process. To assure cost savings without losing yield, and ensure cost-effective IC assembly, optimization of the parameters for the Cu wire process is critical. This work proposes a hybrid intelligent approach to derive robust parameter settings for a fine-pitch Cu wire bonding process with multiple quality characteristics. The proposed methodology utilizes grey relational analysis and an entropy measurement method to convert the multiple responses into a single synthetic performance index without involving the subjective judgment of an engineer and causing unbalanced improvements of the responses. An integrated neural network model and genetic algorithm method is then applied to acquire the optimal parameter settings. The performance of this method is evaluated experimentally and the results compared with that of the response surface methodology and original parameter settings. The results confirm the feasibility and practicality of this strategy to improve production yield and process capability during Cu wire bonding.