This study looks at improving the service quality in nursing homes as well as the intricate relationships between various factors. We use two research models herein. First, Interpretive Structural Modeling (ISM) establishes the criteria for the interrelationship structure, categorized according to their driving power and dependence. This methodology provides a means by which order can be imposed on the complexity of such criteria. Insights from this model can help top managers in strategic planning to improve the service quality in nursing home care. Second, because ISM does not provide any weighting associated with the criteria, we employ the Analytic Network Process (ANP) approach to calculate the weighted importance of the key factors and to identify those factors impacting the service quality of nursing home care.
In this paper, on the basis of the conception of symmetric fuzzy number and linear
planning theory as well as the maximum-minimum and mean values of the measured cutting force, two fuzzy prediction models of cutting force uncertainty are constructed. The test results show that the proposed fuzzy models can validly predict the variation ranges of cutting force uncertainty for the given cutting conditions, and better express the uncertainty of cutting force than the empirical model from the least square regression.
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