A dynamic network-based decision architecture for performance evaluation and improvement

2020 ◽  
Vol 39 (3) ◽  
pp. 4299-4311
Author(s):  
Kuang-Hua Hu ◽  
Sin-Jin Lin ◽  
Ming-Fu Hsu ◽  
Fu-Hsiang Chen
Keyword(s):  
Performance Evaluation ◽  
Dynamic Network ◽  
Knowledge Generation ◽  
Forecasting Model ◽  
Integration Step ◽  
Stage Model ◽  
Functional Link ◽  
Promising Alternative ◽  
Performance Forecasting ◽  
Set Up

This study introduces a dynamic decision architecture that involves three steps for corporate performance forecasting as such bad performance has been widely recognized as the main trigger for a financial crisis. Step-1: performance evaluation and integration; Step-2: forecasting model construction; and Step-3: knowledge generation. First, the decision making trial and evaluation laboratory (DEMATEL) is incorporated with balanced scorecards (BSC) to discover the complicated/intertwined relationships among BSC’s four perspectives. To overcome the problem of BSC that cannot yield a specific direction, the study then employs data envelopment analysis (DEA). Apart from previous studies that utilize an all embracing one-stage model, this set-up extends it to a two-stage model that calculates the performance scores for each BSC perspective. By doing so, users can realize a company’s weaknesses and strengths and identify possible paths toward efficiency. VIKOR is subsequently used to summarize all scores into a synthesized one. Second, the analyzed outcomes are then fed into random vector functional-link (RVFL) networks to establish the forecasting model. To handle the opaque nature of RVFL, the instance learning method is conducted to extract the implicit decision logics. Finally, the introduced architecture, tested by real cases, offers a promising alternative for performance evaluation and forecasting.

scholarly journals NEWS-BASED SOFT INFORMATION AS A CORPORATE COMPETITIVE ADVANTAGE

2019 ◽  
Vol 26 (1) ◽  
pp. 48-70 ◽  
Author(s):  
Ming-Fu Hsu ◽  
Te-Min Chang ◽  
Sin-Jin Lin
Keyword(s):  
Decision Making ◽  
Soft Information ◽  
Forecasting Model ◽  
Data Envelopment ◽  
Financial Indicators ◽  
Promising Alternative ◽  
Hard Information ◽  
Different Types ◽  
Performance Forecasting ◽  
Information Supply Chain

This study establishes a decision-making conceptual architecture that evaluates decision making units (DMUs) from numerous aspects. The architecture combines financial indicators together with a variety of data envelopment analysis (DEA) specifications to encapsulate more information to give a complete picture of a corporate’s operation. To make outcomes more accessible to non-specialists, multidimensional scaling (MDS) was performed to visualize the data. Most previous studies on forecasting model construction have relied heavily on hard information, with quite a few works taking into consideration soft information, which contains much denser and more diverse messages than hard information. To overcome this challenge, we consider two different types of soft information: supply chain influential indicator (SCI) and sentimental indicator (STI). SCI is computed by joint utilization of text mining (TM) and social network analysis (SNA), with TM identifying the corporate’s SC relationships from news articles and SNA to determining their impact on the network. STI is extracted from an accounting narrative so as to comprehensively illustrate the relationships between pervious and future performances. The analyzed outcomes are then fed into an artificial intelligence (AI)-based technique to construct the forecasting model. The introduced model, examined by real cases, is a promising alternative for performance forecasting.

scholarly journals Modeling, Simulation and Uncertain Optimization of the Gun Engraving System

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 398
Author(s):  
Tong Xin ◽  
Guolai Yang ◽  
Fengjie Xu ◽  
Quanzhao Sun ◽  
Alexandi Minak
Keyword(s):  
Performance Evaluation ◽  
Optimal Design ◽  
Resistance Force ◽  
Gas Temperature ◽  
Evaluation Indexes ◽  
Uncertain Optimization ◽  
Design Variables ◽  
Set Up ◽  
Rotating Band ◽  
Percent Decrease

The system designed to accomplish the engraving process of a rotating band projectile is called the gun engraving system. To obtain higher performance, the optimal design of the size parameters of the gun engraving system was carried out. First, a fluid–solid coupling computational model of the gun engraving system was built and validated by the gun launch experiment. Subsequently, three mathematic variable values, like performance evaluation indexes, were obtained. Second, a sensitivity analysis was performed, and four high-influence size parameters were selected as design variables. Finally, an optimization model based on the affine arithmetic was set up and solved, and then the optimized intervals of performance evaluation indexes were obtained. After the optimal design, the percent decrease of the maximum engraving resistance force ranged from 6.34% to 18.24%; the percent decrease of the maximum propellant gas temperature ranged from 1.91% to 7.45%; the percent increase of minimum pressure wave of the propellant gas ranged from 0.12% to 0.36%.

Early Capacity Testing of an Enterprise Service Bus

2013 ◽  
pp. 288-305
Author(s):  
Ken Ueno ◽  
Michiaki Tatsubori
Keyword(s):  
Performance Evaluation ◽  
Capacity Planning ◽  
Service Oriented Architecture ◽  
Service Providers ◽  
System Development ◽  
Resource Assessment ◽  
Enterprise Service Bus ◽  
Hardware Resource ◽  
And Performance ◽  
Set Up

An enterprise service-oriented architecture is typically done with a messaging infrastructure called an Enterprise Service Bus (ESB). An ESB is a bus which delivers messages from service requesters to service providers. Since it sits between the service requesters and providers, it is not appropriate to use any of the existing capacity planning methodologies for servers, such as modeling, to estimate the capacity of an ESB. There are programs that run on an ESB called mediation modules. Their functionalities vary and depend on how people use the ESB. This creates difficulties for capacity planning and performance evaluation. This article proposes a capacity planning methodology and performance evaluation techniques for ESBs, to be used in the early stages of the system development life cycle. The authors actually run the ESB on a real machine while providing a pseudo-environment around it. In order to simplify setting up the environment we provide ultra-light service requestors and service providers for the ESB under test. They show that the proposed mock environment can be set up with practical hardware resources available at the time of hardware resource assessment. Our experimental results showed that the testing results with our mock environment correspond well with the results in the real environment.

An Integrated CRO and FLANN Based Classifier for a Non-Imputed and Inconsistent Dataset

2019 ◽  
Vol 28 (03) ◽  
pp. 1950013 ◽  
Author(s):  
Ch. Sanjeev Kumar Dash ◽  
Ajit Kumar Behera ◽  
Sarat Chandra Nayak ◽  
Satchidananda Dehuri ◽  
Sung-Bae Cho
Keyword(s):  
Missing Values ◽  
Probabilistic Approach ◽  
Integrated Approach ◽  
Bench Mark ◽  
Chemical Reaction Optimization ◽  
Functional Link ◽  
Missing Value ◽  
Promising Alternative ◽  
Reaction Optimization ◽  
Selection Of

This paper presents an integrated approach by considering chemical reaction optimization (CRO) and functional link artificial neural networks (FLANNs) for building a classifier from the dataset with missing value, inconsistent records, and noisy instances. Here, imputation is carried out based on the known value of two nearest neighbors to address dataset plagued with missing values. The probabilistic approach is used to remove the inconsistency from either of the datasets like original or imputed. The resulting dataset is then given as an input to boosted instance selection approach for selection of relevant instances to reduce the size of the dataset without loss of generality and compromising classification accuracy. Finally, the transformed dataset (i.e., from non-imputed and inconsistent dataset to imputed and consistent dataset) is used for developing a classifier based on CRO trained FLANN. The method is evaluated extensively through a few bench-mark datasets obtained from University of California, Irvine (UCI) repository. The experimental results confirm that our preprocessing tasks along with integrated approach can be a promising alternative tool for mitigating missing value, inconsistent records, and noisy instances.

scholarly journals Development of a rotational set‐up correction device for stereotactic head radiation therapy: A performance evaluation

2019 ◽  
Vol 20 (6) ◽  
pp. 206-212
Author(s):  
Keisuke Usui ◽  
Akira Isobe ◽  
Naoya Hara ◽  
Tomoya Muroi ◽  
Osamu Sajiki ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Performance Evaluation ◽  
Set Up ◽  
A Performance

A Neural Network Forecasting Model of Beijing Motor Vehicles Sold Based on Set Pare Analysis

2011 ◽  
Vol 403-408 ◽  
pp. 2333-2336
Author(s):  
Xiao Xi Zhang ◽  
Ding Tian Zhang
Keyword(s):  
Development Trend ◽  
Motor Vehicles ◽  
Forecasting Model ◽  
Connection Number ◽  
Forecasting Accuracy ◽  
Principal Factors ◽  
Set Up ◽  
Partial Connection ◽  
Instance Analysis ◽  
The Development Trend

By applying neural networks to forecasting Beijing motor vehicles sold, sequencing the principal factors and analyzing the development trend using connection number and partial connection number of the set pair analysis (SPA), we set up the forecasting model of Beijing motor vehicles sold. The instance analysis shows that it is a scientific and suitable system analyzing method of high forecasting accuracy.

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