A combination of QFD and imprecise DEA with enhanced Russell graph measure: A case study in healthcare

Quality function deployment (QFD) is a proven tool for process and product development, which translates the voice of customer (VoC) into engineering characteristics (EC), and prioritizes the ECs, in terms of customer's requirements. Traditionally, QFD rates the design requirements (DRs) with respect to customer needs, and aggregates the ratings to get relative importance scores of DRs. An increasing number of studies stress on the need to incorporate additional factors, such as cost and environmental impact, while calculating the relative importance of DRs. However, there is a paucity of methodologies for deriving the relative importance of DRs when several additional factors are considered. Ramanathan and Yunfeng [43] proved that the relative importance values computed by data envelopment analysis (DEA) coincide with traditional QFD calculations when only the ratings of DRs with respect to customer needs are considered, and only one additional factor, namely cost, is considered. Also, Kamvysi et al. [27] discussed the combination of QFD with analytic hierarchy process–analytic network process (AHP–ANP) and DEAHP–DEANP methodologies to prioritize selection criteria in a service context. The objective of this paper is to propose a QFD–imprecise enhanced Russell graph measure (QFD–IERGM) for incorporating the criteria such as cost of services and implementation easiness in QFD. Proposed model is applied in an Iranian hospital.     

Fuzzy Stress-based Modeling for Probabilistic Irrigation Planning Using Copula-NSPSO

Hydrological uncertainties are the main components of a sustainable framework in agricultural water management. Prediction of drought as a meteorological phenomenon should be considered to define the groundwater exploitation strategies. This study was conducted to develop a multiobjective-bivariate structure for reducing the soil moisture deficit and groundwater withdrawal in the Qazvin Irrigation District, Qazvin province, Iran. Therefore, non-dominated sorting theory, self-organizing particle swarm optimization and bivariate copula functions were incorporated under fuzzy uncertainty analysis. The results showed that the generalized extreme values and log-normal distribution functions had the best fitness on the drought peak and severity with Kolmogorov Smirnov amounts of 0.08 and 0.17, respectively. Furthermore, the goodness-of-fit tests were indicated the Joe joint function (MLE = 11) is the appropriate function for estimating the probabilistic values of drought characteristics. Proposed plans were to increase the water use efficiency for improving the expected yield production by an average of 20%. Furthermore, the standardized groundwater index was decreased from 1.1 to –4.3 for winter crops.

     

Prediction of Surface Flow by Forcing of Climate Forecast System Reanalysis Data

Meteorological data are key variables for hydrologists to simulate the rainfall-runoff process using hydrological models. The collection of meteorological variables is sophisticated, especially in arid and semi-arid climates where observed time series are often scarce. Climate Forecast System Reanalysis (CFSR) Data have been used to validate and evaluate hydrological modeling throughout the world. This paper presents a comprehensive application of the Soil and Water Assessment Tool (SWAT) hydrologic simulator, incorporating CFSR daily rainfall-runoff data at the Roodan study site in southern Iran. The developed SWAT model including CFSR data (CFSR model) was calibrated using the Sequential Uncertainty Fitting 2 algorithm (SUFI-2). To validate the model, the calibrated SWAT model (CFSR model) was compared with the observed daily rainfall-runoff data. To have a better assessment, terrestrial meteorological gauge stations were incorporated with the SWAT model (Terrestrial model). Visualization of the simulated flows showed that both CFSR and terrestrial models have satisfactory correlations with the observed data. However, the CFSR model generated better estimates regarding the simulation of low flows (near zero). The results of the uncertainty analysis showed that the CFSR model predicted the validation period more efficiently. This might be related with better prediction of low flows and closer distribution to observed flows. The Nash-Sutcliffe (NS) coefficient provided good- and fair-quality modeling for calibration and validation periods for both models. Overall, it can be concluded that CFSR data might be promising for use in the development of hydrological simulations in arid climates, such as southern Iran, where there are shortages of data and a lack of accessibility to the data.     

Application of New Hybrid Optimization Technique for Parameter Estimation of New Improved Version of Muskingum Model

The Muskingum method is one of the most utilized lumped flood routing model in which calibration of its parameters provides an active area of research in water resources engineering. Although various techniques and versions of Muskingum model have been presented to estimate the parameters of different versions of Muskingum model, more rigorous approaches and models are still required to improve the computational precision of calibration process. In this study, a new hybrid technique was proposed for Muskingum parameter estimation which combines the Modified Honey Bee Mating Optimization (MHBMO) and Generalized Reduced Gradient (GRG) algorithms. According to the conducted literature-review on the improvement of Muskingum flood routing models, a new six-parameter Muskingum model was proposed. The hybrid technique was successfully applied for parameter estimation of this new version of Muskingum model for three case studies selected from literature. The obtained results were compared with those of other methods using several common performance evaluation criteria. The new hybrid method with the new proposed Muskingum model perform the best among all the considered approaches based on most of utilized criteria. The new Muskingum model significantly reduces the SSQ value for the double-peak case study. Finally, the achieved results demonstrate that not only the hybrid MHBMO-GRG algorithm overcomes the shortcomings of both phenomenon-mimicking and mathematical optimization techniques, but also the presented Muskingum model is appeared to be the most reliable version of Muskingum model comparing with other considered models in this research.     

Some unified characterization results on the generalized Pareto distributions based on generalized order statistics

In the present study we extend and unify some existing results in the literature on characterization of the generalized Pareto distributions based on generalized order statistics.     

Trend Analysis in Reference Evapotranspiration Using Mann-Kendall and Spearman’s Rho Tests in Arid Regions of Iran

In this research, temporal trends of reference evapotranspiration (ET0) values were investigated in arid regions of Iran. For this purpose, meteorological observations collected from 11 high quality meteorological sites over a 41-year period (1965–2005) were used and statistically significant ET0 trends in the monthly, seasonal and annual time basis were detected using nonparametric Mann-Kendall (MK) and Spearman’s Rho (SR) tests at the 5% significant level. To eliminate the effect of serial correlation on the test results, the Trend Free Pre-Whitening (TFPW) approach was applied. The results of this study indicated that the ET0 trends for some cities were increasing (positive) however, for some sites, they showed decreasing (negative) trends. The most number of significant ET0 trends on a monthly time scale occurred at Birjand but no significant trend was observed at the Bandarabbas, Sabzevar and Semnan sites. On the annual time scale, Mashhad revealed the highest positive trend (7.5 mm per year). On the other hand, Esfahan showed the highest decreasing (negative) ET0 trend of about −6.38 mm per year. In general, the performances and abilities of the MK and SR tests were consistent at the verified significant level.     

The Effect of Normative Expectations on Employees’ Intrinsic Interest in Creativity: Is There also a Mediating Mechanism?

As the core element of a firm’s innovation capabilities and the source of firm growth, increasing employee creativity is a primary goal of organizations. Based on social cognitive theory, role theory, and creativity theory, this paper investigates the relationship between perception of normative expectations and employees’ intrinsic interest in creativity, as well as explores the role of self-efficacy in this relationship. We use data from a survey questionnaire of 2,035 employees from 185 firms in different geographic locations across China. Our empirical findings reveal that when employees perceive a normative expectation coming from leaders, family or customers, coupled with the fear of failing to satisfy the expectations of those referent groups, it positively and significantly affects their intrinsic interest in creativity. In addition, self-efficacy not only positively and significantly affects employees’ intrinsic interest in creativity, but also plays a partially mediating role in the relationship between the perception of expectation and employees’ intrinsic interest in creativity.     

A DEA model for resource allocation

This paper concerns inverse DEA. The aim is to estimate input/output levels of a given Decision Making Unit (DMU) when some or all of its input/output levels are changed, under preserving the efficiency index. We show that in the case of estimating increased required input vector when the output vector is increased, the current method which uses weakly efficient solution of the relevant multiple objective optimization problem may fail. We propose some sufficient conditions for input estimation.     

Dryland river regime shifts in Iran: Drivers and feedbacks

In the Anthropocene, human activities have created unprecedented changes and nonlinear relationships between humans and nature. These changes can be much faster and more intense in arid and semiarid areas that have been affected by intense human activities. Iran has climates from very humid to very dry, but arid and semiarid climates cover the country's largest area. Many of these arid areas have undergone severe changes in their surface and groundwater ecosystems in recent years, which have caused severe damage to humans and the environment in the area and surrounding areas. Therefore, in this study, using the theory of regime shifts, the time series of the Zayandeh-Rud River Basin in the center of Iran were analyzed. First, the data of the desired time series in the period of 1986–2018 was arranged seasonally. Then, using the sequential t-test method, regime shifts in these time series were identified, and then, causal loop diagrams of these shifts and their drivers and feedbacks were interpreted. The results showed that in the time series of quantity and quality of surface water and groundwater level in the studied stations and aquifers, regime shifts can be identified. Regime shifts were also identified in the time series of agricultural land area. These shifts have occurred with the increase in human activities since the early 1950s in the metropolis of Isfahan, the increase in agricultural and industrial exploitation, and consequently, the increase in population. When this reinforcing feedback loop becomes dominant, the Zayandeh-Rud River system has shifted from a regime of rich water resources to a regime of poor water resources. However, by recognizing and systematically analyzing these shifts, the Zayandeh-Rud River system can be directed toward a sustainable system through structural reform, negotiation, and redefining goals.