Abstract: With the development of business competition, it is important for healthcare providers to improve their service qualities. In order to improve service quality of a clinic, four important dimensions are defined: tangibles, responsiveness, empathy, and reliability. Moreover, there are several service stages in hospitals such as financial screening and examination. One of the most challenging limitations for improving service quality is budget which impressively affects the service quality. In this paper, we present an approach to address budget uncertainty and provide guidelines for service resource allocation. In this paper, a service quality improvement approach is proposed which can be adopted to multistage service processes to improve service quality, while controlling the costs. A multi-objective function based on the importance of each area and dimension is defined to link operational variables to service quality dimensions. The results demonstrate that our approach is not ultra-conservative and it shows the actual condition very well. Moreover, it is shown that different strategies can affect the number of employees in different stages.
Abstract: Atmospheric stability plays the most important role in
the transport and dispersion of air pollutants. Different methods are
used for stability determination with varying degrees of complexity.
Most of these methods are based on the relative magnitude of
convective and mechanical turbulence in atmospheric motions.
Richardson number, Monin-Obukhov length, Pasquill-Gifford
stability classification and Pasquill–Turner stability classification, are
the most common parameters and methods. The Pasquill–Turner
Method (PTM), which is employed in this study, makes use of
observations of wind speed, insolation and the time of day to classify
atmospheric stability with distinguishable indices. In this study, a
model is presented to determination of atmospheric stability
conditions using PTM. As a case study, meteorological data of
Mehrabad station in Tehran from 2000 to 2005 is applied to model.
Here, three different categories are considered to deduce the pattern
of stability conditions. First, the total pattern of stability classification
is obtained and results show that atmosphere is 38.77%, 27.26%,
33.97%, at stable, neutral and unstable condition, respectively. It is
also observed that days are mostly unstable (66.50%) while nights are
mostly stable (72.55%). Second, monthly and seasonal patterns are
derived and results indicate that relative frequency of stable
conditions decrease during January to June and increase during June
to December, while results for unstable conditions are exactly in
opposite manner. Autumn is the most stable season with relative
frequency of 50.69% for stable condition, whilst, it is 42.79%,
34.38% and 27.08% for winter, summer and spring, respectively.
Hourly stability pattern is the third category that points out that
unstable condition is dominant from approximately 03-15 GTM and
04-12 GTM for warm and cold seasons, respectively. Finally,
correlation between atmospheric stability and CO concentration is
achieved.