List of Articles Robust Optimization

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  1 - A Robust Data Envelopment Analysis Method for Business and IT Alignment of Enterprise Architecture Scenarios
  Mehdi Fasanghari Mohsen  Sadegh Amalnick Reza Taghipour Anvari Jafar Razmi
  10.7508/jist.2013.02.004
  Information Technology is recognized as a competitive enabler in today’s dynamic business environment. Therefore, alliance of business and Information Technology process is critical, which is mostly emphasized in Information Technology governance frameworks. On the othe More

  Information Technology is recognized as a competitive enabler in today’s dynamic business environment. Therefore, alliance of business and Information Technology process is critical, which is mostly emphasized in Information Technology governance frameworks. On the other hand, Enterprise Architectures are deployed to steer organizations for achieving their objectives while being responsive to changes. Thus, it is proposed to align the business and Information Technology through investigating the suitability of Enterprise Architecture scenarios. In view of this fact, investigating a flexible decision making method for business and information technology alignment analysis is necessary, but it is not sufficient since the subjective analysis is always perturbed by some degree of uncertainty. Therefore, we have developed a new robust Data Envelopment Analysis technique designed for Enterprise Architecture scenario analysis. Several numerical experiments and a sensitivity analysis are designed to show the performance, significance, and flexibility of the proposed method in a real case. Manuscript profile
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  2 - Portfolio Selection under Trading Constraints and Data Uncertainty Using Robust Optimization Approach and NSGA-II Algorithm
  Pejman Peykani     Alireza Jandaghian
  Portfolio is a collection or combination of investments in financial and non-financial assets that may be carried out by an individual or organization. How to select and optimize of portfolio is very important. One of the most important points that should be considered More

  Portfolio is a collection or combination of investments in financial and non-financial assets that may be carried out by an individual or organization. How to select and optimize of portfolio is very important. One of the most important points that should be considered in the proposed approach for portfolio selection, is uncertainty. Because, one of the most important features of financial markets is their uncertainty. Thus, the purpose of this study is to present a bi-objective model for portfolio selection that is capable to be used under uncertainty of financial data and for this purpose, a robust optimization approach has been used. It should be noted that return and conditional value at risk (CVaR) are considered as model objectives, and the constraints of the number of shares and the purchasing volume of each share have been added to the model. Also, due to the complexity of the proposed model, a NSGA-II meta-heuristic algorithm has been used to solve the suggested model of research. Finally, the presented model was solved by using the actual data of 200 stocks of Tehran stock market for the period of 2017 and the results were analyzed. The results indicate the efficiency of the proposed approach portfolio selection according to the investor's preferences and constraints under uncertainty of financial data. Manuscript profile
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  3 - Grayscale Images Deblurring Using Robust Optimization Problem in Uncertainty Conditions in Blurring Model Parameters
  Zeinab Mohammadi Ebrahim Daneshifar Abbas Ebrahimi moghadam M. Khademi
  Nowadays, one of the most important issues in the field of image processing is image de-blurring. De-blurring of an image can be achieved via two different approaches; blind de-blurring and non-blind de-blurring. In blind de-blurring, the kernel by which the blur has oc More

  Nowadays, one of the most important issues in the field of image processing is image de-blurring. De-blurring of an image can be achieved via two different approaches; blind de-blurring and non-blind de-blurring. In blind de-blurring, the kernel by which the blur has occurred is assumed unknown, while in non-blind de-blurring, this kernel is given. In blind de-blurring, the blurring kernel must be estimated in order to sharpen the corrupted image. This may increase the computational cost of the de-blurring process. Non-blind image de-blurring is an ill-posed problem with linear reverse issues. Therefore, we develop optimization problems in order to estimate the original sharp images. Usually, non-blind de-blurring methods assume that the blurring kernel is error-free, however, in practice our knowledge of the PSF is uncertain. Hence, in this paper, we use a semi-blind method for de-blurring the blurred image that is robust to this uncertainty. The proposed robust optimization model is followed by a filter for image de-blurring that can attain the solution with lowest possible error in the worst case scenarios, that is, the maximum uncertainty about the blurring kernel. Based on the simulation results, our proposed semi-blind model yields more than 4 dB PSNR improvements compared to conventional blind image de-blurring methods. Manuscript profile