This paper considers a portfolio selection problem with normally distributed returns and different rates for borrowing and lending. The primary concern is to determine the amount of investment in different planning horizons when the rate of borrowing is greater than the rate of lending. Chance constrained programming as an appropriate tool for addressing intrinsic uncertainty in portfolio selection problem is used. To solve this nonlinear programming, Genetic Algorithm is utilized. Numerical experiments are performed and the results are analyzed to present the performance of the proposed methodology.
Balanced scorecard is a performance appraisal method planned for measuring the organizational efficiency to develop their strategies. Organization’s strategies in a specified period are the main inputs in this model. Furthermore, due to nature, experts & apos; opinions play the vital role in determining the strategies. In this research, the proposed algorithm is designed by using fuzzy set covering problem and non-linear multiple objective integer programming (zero and one variables), so that it can be useful to choose the best combination of strategies for specified period of time with the least deviation in experts` opinions. The presented model is carried out in Islamic Azad University, Semnan Branch. The results indicate that the designed model can provide the best combination of strategies for entering into the balanced scorecard system.
Many supply chain problems are involved with different parameters, which are under uncertainties. One of the primary concerns on supplier selection is to handle the uncertainty under different circumstances. The primary objective of this paper is to design a model to select suppliers and to determine the amount of purchase from any supplier in the supply chain system. For this purpose, we select the most important criteria using fuzzy questionnaires where the questionnaire uses experts’ opinions in terms of linguistic values. Then, a hierarchy multiple criteria decision-making (MCDM) model based on fuzzy-sets theory is proposed to rank different suppliers and using a goal programming approach, we determine the amount of order product from each supplier. The implementation of the proposed model is demonstrated using a real-world case study.