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- 저자 : Amir Sheikhmohammadi (검색결과 2 건)
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Development of Robust Random Variable for Portfolio Selection Problem
Alireza Ghahtarani,Majid Sheikhmohammady,Amir Abbas Najafi 대한산업공학회 2018 Industrial Engineeering & Management Systems Vol.17 No.4
In this paper, mathematical modeling is developed for portfolio selection problem under uncertainty circumstanceswith regard to a robust stochastic variable. Two popular and common approaches in the area of modeling uncertaintyare robust optimization and stochastic programming. These two methods are used with different considerations in mathematicalmodeling, but each one has a limitation. Stochastic programming assumes a static distribution functionwith static parameters over time for non-deterministic data, and robust optimization considers an indeterminate parameterin a uniform interval around nominal values. Using combination of these two methods can help us to eliminatetheir drawbacks. For this purpose, the concept of a robust stochastic variable has been developed in this research. Thisvariable enables distribution of the uncertainty parameter to vary over time, and its mean, varies from one period toanother; in fact, the parameter of the mean of uncertain probable distribution. The risk measure of CVaR, which allowschanges in mean of uncertainty from time to time, is used to implement the proposed approach. As a numericalexample, the actual data of Tehran Stock Exchange is used for a year as one-month periods. The practical results ofthis research show that the developed method can properly overcome the shortcomings of the previous methods.
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Anoushiravan Mohseni Bandpei,Seyed Mohsen Mohseni,Amir Sheikhmohammadi,Mahdieh Sardar,Maryam Sarkhosh,Mohammad Almasian,Moayad Avazpour,Zahra Mosallanejad,Zahra Atafar,Shahram Nazari,SoheilaRezaei 한국화학공학회 2017 Korean Journal of Chemical Engineering Vol.34 No.2
Arsenic is a critical contaminant for aqueous environments as it poses harmful health risks. To meet the stringent regulations regarding the presence of arsenic in aqueous solutions, the feasibility of montmorillonite clay modified with hexadecyltrimethyl ammonium chloride as the adsorbent was tested for the removal of arsenic ions from aqueous solutions. A scanning electron microscopy (SEM) study confirmed that the organically modified nanoclay (ONC) adsorbent had a porous structure with a vast adsorbent surface. The x-ray fluorescence (XRF) analysis proved the presence of carbon in the structure of the modified nanoclay that can be evidence for the creation of ONC. The x-ray diffraction (XRD) analysis results confirm the existence of four main groups of minerals, carbonate (Calcite), clay (Askmtyt and Kandyt), silicate (Quartz), and phyllosilicate (Kaolinite), in the ONC structure.The influence of various parameters such as solution pH, adsorbent dosage, initial arsenite concentration, and contact time on arsenic adsorption onto ONC was investigated. A 25 full factorial central composite experimental design was applied. A central composite design under response surface methodology (RSM) was employed to investigate the effects of independent variables on arsenite removal and to determine the optimum condition. The experimental values were in a good fit with the ones predicted by the model. The optimal operating points (adsorbent dosage: 3.7 g L−1, surfactant dosage: 3 g L−1 and the contact time: 37.2min) giving maximum arsenite removal (95.95%) were found using Solver “Add-ins” in Microsoft Excel 2010.
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