Algorithm for Increasing the Speed of Evolutionary Optimization and its Accuracy in Multi-objective Problems

Optimization algorithms are important tools for the solution of combinatorial management problems. Nowadays, many of those problems are addressed by using evolutionary algorithms (EAs) that move toward a near-optimal solution by repetitive simulations. Sometimes, such extensive simulations are not possible or are costly and time-consuming. Thus, in this study a method based on artificial neural networks (ANN) is proposed to reduce the number of simulations required in EAs. Specifically, an ANN simulator is used to reduce the number of simulations by the main simulator. The ANN is trained and updated only for required areas in the decision space. Performance of the proposed method is examined by integrating it with the non-dominated sorting genetic algorithm (NSGAII) in multi-objective problems. In terms of density and optimality of the Pareto front, the hybrid NSGAII-ANN is able to extract the Pareto front with much less simulation time compared to the sole use of the NSGAII algorithm. The proposed NSGAII-ANN methodology was examined using three standard test problems (FON, KUR, and ZDT1) and one real-world problem. The latter addresses the operation of a reservoir with two objectives (meeting demand and flood control). Thus, based on this study, use of the NSGAII-ANN integrative algorithm in problems with time-consuming simulators reduces the required time for optimization up to 50 times. Results of the real-world problem, despite lower computational-time requirements, show a performance similar to that achieved in the aforementioned test problems.     

Drought Occurrence Probability Analysis Using Multivariate Standardized Drought Index and Copula Function Under Climate Change

This study aims to investigate the effect of climate change on the probability of drought occurrence in central Iran. To this end, a new drought index called Multivariate Standardized Drought Index (MSDI) was developed, which is composed of the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Soil Moisture Index (SSI). The required data included precipitation, temperature (from CRU TS), and soil moisture (from the ESA CCA SM product) on a monthly time scale for the 1980–2016 period. Moreover, future climate data were downloaded from CMIP6 models under the latest SSPs-RCPs emission scenarios (SSP1-2.6 and SSP5-8.5) for the 2020–2056 period. Based on the normalized root mean square error (NRMSE), Cramer-von mises statistic (S_n), and Nash Sutcliffe (NS) evaluation criteria, the Galambos and Clayton functions were selected to derive copula-based joint distribution functions in both periods. The results showed that more severe and longer droughts will occur in the future compared to the historical period and in particular under the SSP5-8.5 scenario. From the derived joint return period, a drought event with defined severity or duration will happen in a shorter return period as compared with the historical period. In other words, the joint return period indicated a higher probability of drought occurrence in the future period. Moreover, the joint return period analysis revealed that the return period of mild droughts will remain the same, while it will decrease for extreme droughts in the future.

     

The Relationship Between Consumer-Brand Identification and Brand Extension

The present research aims at examining the role of consumer-brand identification (CBI) in attitude toward brand extension regarding the congruency between the values of consumers and brands. In this way, the benefits of CBI to consumers and brand managers are outlined. This research has been conducted through designing a survey and collecting data through a questionnaire. For data mining and investigating the model, the SEM approach is implemented. According to the findings, high levels of CBI lead to positive attitudes toward the brand extension, and that value congruity positively impacts this relationship. The results outline that CBI impacts fit and tie in separate ways, so that CBI effect on fit is more substantial. Furthermore, according to the results, in comparison to the role of tie, the role of fit is more significant in predicting attitudes toward brand extension. The sample from university students is appropriate for testing theory, but limits generalizing the results of the research. In addition, this research has studied one product category and is limited in this perspective. The findings have remarkable recommendations for implementing brand extension as they emphasize the role of perceived fit. Moreover, by reexamining CBI in a less known market, the research has outlined its positive outcomes for firms. This research has used perceived tie in a brand extension context and, similar to perceived fit, has implemented it as another factor to predict attitude toward brand extension. In addition, this research is unique, as it has investigated CBI in a new context.     

Key factors affecting the hospital performance: a qualitative study using fuzzy logic

Identifying the key factors affecting the hospital performance helps better planning for hospital high performance. The purpose of this study is to provide a combination of qualitative and quantitative methods to investigate the hospital performance. In the qualitative section of this study, factors associated with hospital performance were detected using literature review, interviews, and expert panels. The findings analyzed by one sample t test and categorized by framework analysis method. In the quantitative section of this study, both direct and indirect relationships between factors were measured by using fuzzy Decision Making Trial and Evaluation Laboratory technique to detect influencing and influenced factors. Finally the key factors affecting the hospital performance were detected.     

HAZARD PROCESSES AND MARTINGALE HAZARD PROCESSES

In this paper, we build a bridge between different reduced‐form approaches to pricing defaultable claims. In particular, we show how the well‐known formulas by Duffie, Schroder, and Skiadas and by Elliott, Jeanblanc, and Yor are related. Moreover, in the spirit of Collin Dufresne, Hugonnier, and Goldstein, we propose a simple pricing formula under an equivalent change of measure. Two processes will play a central role: the hazard process and the martingale hazard process attached to a default time. The crucial step is to understand the difference between them, which has been an open question in the literature so far. We show that pseudo‐stopping times appear as the most general class of random times for which these two processes are equal. We also show that these two processes always differ when τ is an honest time, providing an explicit expression for the difference. Eventually we provide a solution to another open problem: we show that if τ is an arbitrary random (default) time such that its Azéma's supermartingale is continuous, then τ avoids stopping times.     

A reading guide for last passage times with financial applications in view

In this survey on last passage times, we propose a new viewpoint which provides a unified approach to many different results which appear in the mathematical finance literature and in the theory of stochastic processes. In particular, we are able to improve the assumptions under which some well-known results are usually stated. Moreover we give some new and detailed calculations for the computation of the distribution of some large classes of last passage times. We have kept in this survey only the aspects of the theory which we expect potentially to be relevant for financial applications.     

Default times, no-arbitrage conditions and changes of probability measures

In this paper, we give a financial justification, based on no-arbitrage conditions, of the (H)-hypothesis in default time modeling. We also show how the (H)-hypothesis is affected by an equivalent change of probability measure. The main technique used here is the theory of progressive enlargements of filtrations.     

Deriving and Evaluating Bathymetry Maps and Stage Curves for Shallow Lakes Using Remote Sensing Data

Urmia Lake as a most vital water bodies in Iran, has been shrinking since the late twentieth century and its area has dramatically decreased. To develop and apply any plans to survive the lake, qualitative and quantitative analysis and any modeling, deriving physical information such as volume, area and their changes are very crucial. The objectives of this study were therefore, intended firstly, to study the bathymetry of Urmia Lake with a more satisfactory approach using Landsat- LDCM satellite image and in situ measurement data. The polynomial model was developed to predict the water depth in Urmia Lake area. This model was developed with the input series of reflectance values from blue, green, red and NIR bands in the Landsat- LDCM satellite imagery for Urmia Lake taken on 12 April 2013 of the sampling sites from actual depth measured were taken on the same date. Also, using a large archive of Landsat imagery (TM, ETM+ and LDCM), a counter of equivalent elevation were established for deriving the bathymetry of desiccated areas by mapping the edges of the lake and finally assembled with bathymetry derived from polynomial model. In-situ depth measurements were used to evaluate resultant derived bathymetric map. This comparison shows reasonable agreement between the Landsat-derived depths and those measured in the field with RMSE of 0.27 cm and R² = 0.91. The maximum and mean depths measured were 4.9 and 11 m respectively. The maximum depth measured was located at the upper part of the lake. As well as, developed multi-regression equation used for deriving another bathymetry map using Landsat- LDCM satellite image taken on Sep. 2015 for salt deposition monitoring. Results indicates that about 64 cm salt deposition has occurred during the last two years. Secondly, to make stage curves of lake, multi-temporal changes of water body have been derived from Landsat, MODIS and AVHRR satellite images sets since 1972. In this regard, the area of Urmia Lake at different level was estimated base on object oriented and pixel base classification using 78 satellite images. Finally, stage curve (volume- area- level relations) was derived from bathymetry map.     

Debunking the myth of general consumer rejection of green genetic engineering: Empirical evidence from Germany

The emergence of a more sustainable economy in Europe was accompanied by a range of bio‐based products and technologies. As a prominent example, green genetic engineering opens up multiple options to increase agricultural production, but its public acceptance seems to vary by application area. Risk perception explains consumer acceptance of green genetic engineering, which is a necessary precondition for wider technology adoption. This study investigates risk perceptions for four major sources of risk: health related, environmental, socioeconomic and ethical. Data were collected in a laboratory experiment in Germany, with a total of 439 participants. A between‐subject design was employed. The four experimental treatment groups comprised two policy scenarios, namely one only permitting research and development and the other allowing full commercialization of genetically modified products, and two product end‐uses, bioenergy and food. The study shows significant end‐use differences in both the type of policy scenario and the risk dimension in question. In particular, health risks were generally perceived to be lower for bioenergy than food whenever full commercialization was pursued. Furthermore, full commercialization of genetically modified food prompted higher concerns about personal health, whereas use of crops for bioenergy production was broadly related to higher levels of socioeconomic risk. Finally, although the majority of consumers identified health risks as being most relevant, the consequences for the environment evoked the greatest degree of risk perception. In general, our findings lend support for the notion that the policy regime is the most important determinant for risk perception, followed by the type of risk dimension and level of trust in industry.