A Quality Optimization Method for Service Process Model

2009 ◽  
Author(s):  
Haiyan Zhao ◽  
Jian Cao ◽  
Xiaohan Sun
Keyword(s):  
Process Model ◽  
Optimization Method ◽  
Service Process ◽  
Quality Optimization

scholarly journals Semiparametric likelihood inference for heterogeneous survival data under double truncation based on a Poisson birth process

2021 ◽  
Author(s):  
Achim Dörre
Keyword(s):  
Survival Data ◽  
Process Model ◽  
Optimization Method ◽  
Finite Sample ◽  
Birth Process ◽  
Dimensional Parameter ◽  
Lifetime Distributions ◽  
Computational Performance ◽  
Semiparametric Likelihood ◽  
Double Truncation

AbstractWe study a selective sampling scheme in which survival data are observed during a data collection period if and only if a specific failure event is experienced. Individual units belong to one of a finite number of subpopulations, which may exhibit different survival behaviour, and thus cause heterogeneity. Based on a Poisson process model for individual emergence of population units, we derive a semiparametric likelihood model, in which the birth distribution is modeled nonparametrically and the lifetime distributions parametrically, and define maximum likelihood estimators. We propose a Newton–Raphson-type optimization method to address numerical challenges caused by the high-dimensional parameter space. The finite-sample properties and computational performance of the proposed algorithms are assessed in a simulation study. Personal insolvencies are studied as a special case of double truncation and we fit the semiparametric model to a medium-sized dataset to estimate the mean age at insolvency and the birth distribution of the underlying population.

A Process Algebra Based Method for Value Analysis on Service Process Model

2010 ◽  
Vol 33 (11) ◽  
pp. 2177-2189 ◽  
Author(s):  
Chao MA ◽  
Xiao-Fei XU ◽  
Zhong-Jie WANG
Keyword(s):  
Process Model ◽  
Process Algebra ◽  
Service Process ◽  
Value Analysis

Research on Behavior and Connotation of Product Design

2011 ◽  
Vol 308-310 ◽  
pp. 1706-1709
Author(s):  
Xing Guo Ma ◽  
Bang Chun Wen
Keyword(s):  
Product Design ◽  
Process Model ◽  
Design Thinking ◽  
Service Process ◽  
Behavior Model ◽  
Systematic Design ◽  
Key Factors ◽  
Quality Cost ◽  
Design Process Model ◽  
Set Up

Based on studying design behavior and design thinking, a design behavior model which dealt with person, QCTS (quality, cost, time and service), process and environment in product design was set up. The key factors and connotation of product design were discussed based on the model. An expanded design environments model and a systematic design process model were set up. The results show that person, product, process and environment are key factors of product design; in design, the person is body of thinking, QCTS is goal, the process is behavior formula, and the environment is restraint; the product design is a process driven by customers’ requirements in which the knowledge is used and is materialized based on brain, and abstract or detail concepts are changed to a physical assembly or a product step by step.

scholarly journals Optimization of a Cyclone Using Multiphase Flow Computational Fluid Dynamics

2019 ◽  
Author(s):  
Justin Weber ◽  
William Fullmer ◽  
Aytekin Gel ◽  
Jordan Musser
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Multiphase Flow ◽  
Process Model ◽  
Vortex Tube ◽  
Optimization Method ◽  
Department Of Energy ◽  
Original Design ◽  
Modeling And Analysis ◽  
Cfd Models

Abstract The US Department of Energy (DOE) National Energy Technology Laboratory’s (NETL) 50 kWth chemical looping reactor has an underperforming cyclone, designed using empirical correlations. To improve the performance of this cyclone, the vortex tube radius and length, barrel radius, and the inlet width and height are optimized using computational fluid dynamics (CFD). For this work, NETL’s open source Multiphase Flow with Interphase eXchange (MFiX) CFD code has been used to model a series of cyclones with varying geometric differences. To perform the optimization process, the surrogate modeling and analysis toolset inside Nodeworks was used. The basic methodology for the process is to use a design of experiments method (optimal Latin Hypercube) to generate samples that fill the design space. CFD models are then created, executed, and post-processed. A response surface (Gaussian process model) is created to characterize the relationship between input parameters and the Quantities of interest (QoI). Finally, the CFD-surrogate is used by an optimization method (differential evolution) to find the optimal design condition. The resulting optimal cyclone has a larger diameter and longer vortex tube, a larger diameter barrel, and a taller and narrower solids inlet. The improved design has a predicted pressure drop 11-times lower than the original design while reducing the mass loss by a factor of 2.3.

Multidisciplinary Design Optimization Method and Algorithm Based on iSIGHT

2011 ◽  
Vol 284-286 ◽  
pp. 962-965
Author(s):  
Cheng Long Wang ◽  
Qing Liang Zeng ◽  
Ru Jun Han ◽  
Li Ren
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Process Model ◽  
Optimization Method ◽  
Multidisciplinary Design ◽  
Algorithm Optimization ◽  
General Process ◽  
Engineering Problems ◽  
Practical Engineering

Basing on the introduction of Multidisciplinary Design Optimization (MDO), Multidisciplinary Design Optimization method based on iSIGHT is given, which includes one general process model and one optimization algorithm. Optimization of one bearing is selected as one example. According to its application, it approves that MDO methods can solve practical engineering problems more effectively because of comprehensive consideration of the internal problems in all disciplines.

A service process optimization method based on model refinement

2010 ◽  
Vol 63 (1) ◽  
pp. 72-88 ◽  
Author(s):  
Jian Cao ◽  
Jie Wang ◽  
Haiyan Zhao ◽  
Xiaohan Sun
Keyword(s):  
Process Optimization ◽  
Optimization Method ◽  
Service Process ◽  
Model Refinement
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