Abstract
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.
Highlights
The development of simulators can be traced back to 1929, when Edward Link developed a mechanical flight simulator that was aimed at helping new pilots familiarize with flight operating procedures
Training simulators are characterized by various advantages; for example, using such training systems, it mitigates the risks of training casualties, saves on training costs, reduces equipment wear, facilitates autonomous learning, enhances attitude toward learning, enables exposure to battle sites, and increase training effectiveness
Chang et al [64] applied the 2-tuple fuzzy linguistic model to convert the linguistic information into numerical values (e.g., [s6,0.5]), and adopted the same method for converting the linguistic information of other items
Summary
The development of simulators can be traced back to 1929, when Edward Link developed a mechanical flight simulator that was aimed at helping new pilots familiarize with flight operating procedures. Advancements in technologies have engendered a diversity of simulators that can be extensively applied in many fields, such as power electronics [1], electronic applications [2], materials [3], drive security [4], traffic research [5], flight security [6], helicopter pilot training [7], performance evaluation of maritime pilots [8], aviation pilot training [9], and medical education [10] Studies in these fields have yielded informative outcomes
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