Browsing by Author "Chung, Hsiang-Yu"

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A Novel AHP-Based Benefit Evaluation Model of Military Simulation Training Systems
(2015-01-01)
Chang, Kuei-Hu
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Chang, Yung-Chia
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Chung, Hsiang-Yu
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工業工程與管理學系
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Department of Industrial Engineering and Management
With the constantly changing patterns of war, more technologically exquisite weapons are designed, increasing in cost and complexity. Training maneuvers with live ammunition are expensive and are prone to accidental casualties. Thus, many countries are gradually adopting simulation training systems to replace some actual exercises to reduce casualties and still maintain maximum combat readiness. However, each simulation training system has a different background with regard to time, source, function goal, and quality of environment and staff. It is also more difficult to assess the benefit of simulation training systems. Moreover, traditional benefit assessments of military simulation training systems have merely considered their efficiency, not safety, causing biased conclusions. To solve these issues, this paper integrates the analytic hierarchy process (AHP), important-performance analysis (IPA), and the 2-tuple fuzzy linguistic representation model to determine the benefits of simulation training systems. To verify the proposed approach, a numerical example of the evaluation of a training simulator system's benefit is adopted. Compared with the traditional AHP method, the proposed method does not lose any valuable information that is provided by experts and also considers training safety. Further, these data are presented in 2-dimensional graphs for managers to further guide the decision making process.
Integrating Soft Set Theory and Fuzzy Linguistic Model to Evaluate the Performance of Training Simulation Systems
(2016-09-06)
Chang, Kuei-Hu
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Chang, Yung-Chia
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Chain, Kai
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Chung, Hsiang-Yu
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工業工程與管理學系
The advancement of high technologies and the arrival of the information age have caused changes to the modern warfare. The military forces of many countries have replaced partially real training drills with training simulation systems to achieve combat readiness. However, considerable types of training simulation systems are used in military settings. In addition, differences in system set up time, functions, the environment, and the competency of system operators, as well as incomplete information have made it difficult to evaluate the performance of training simulation systems. To address the aforementioned problems, this study integrated analytic hierarchy process, soft set theory, and the fuzzy linguistic representation model to evaluate the performance of various training simulation systems. Furthermore, importance-performance analysis was adopted to examine the influence of saving costs and training safety of training simulation systems. The findings of this study are expected to facilitate applying military training simulation systems, avoiding wasting of resources (e.g., low utility and idle time), and providing data for subsequent applications and analysis. To verify the method proposed in this study, the numerical examples of the performance evaluation of training simulation systems were adopted and compared with the numerical results of an AHP and a novel AHP-based ranking technique. The results verified that not only could expert-provided questionnaire information be fully considered to lower the repetition rate of performance ranking, but a two-dimensional graph could also be used to help administrators allocate limited resources, thereby enhancing the investment benefits and training effectiveness of a training simulation system.
Soft failure mode and effects analysis using the OWG operator and hesitant fuzzy linguistic term sets
(2018-01-01)
Chang, Kuei-Hu
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Wen, Ta-Chun
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Chung, Hsiang-Yu
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工業工程與管理學系
;
Department of Industrial Engineering and Management
Risk assessment is a critical part of reliability engineering and is vital to the success of an enterprise's performance. Traditionally, risk assessment has used the risk priority number (RPN) to evaluate the risk of failure. Unfortunately, the traditional RPN method has several shortcomings-it loses partially valuable information when some data are missing or nonexistent and does not consider the ordered weight between the severity, occurrence, and detection indicators; further, it does not consider the direct and indirect relationships between failure modes and causes of failure, and it has a high duplication rate. To resolve these issues, this paper integrates the ordered weighted geometric (OWG) operator and hesitant fuzzy linguistic term sets to increase the effectiveness of failure mode and effects analysis (FMEA), named soft FMEA. An empirical case of extreme low-k (ELK) dielectric integration is used to illustrate the proposed method and demonstrate its value. Our results show that the soft FMEA method is applicable to real-world situations and constitutes a more general FMEA method.