Portfolio selection is one of the most important financial and investment issues. Portfolio selection seeks to allocate a predetermined capital (wealth) over one or multiple time periods between assets and stocks in a such way that the wealth of investor (portfolio owner) is maximized the risks are minimized. In the paper, we first propose a mathematical Programming model for Portfolio selection to maximize the minimum amount Sharpe ratios of portfolio in all periods (max-min problem). Then, due to the uncertain property of the input parameters of such a problem, a Robust Possibilistic Programming model (based on necessity theory) has been developed, which is capable of adjusting the Robust degree of output decisions to the uncertainty of the parameters. The proposed model has been tested on 27 companies active in the Tehran stock market. At the end, the results of the model demonestrate the good performance of the Robust Possibilistic Programming model.

one of the most important financial and investment issues is Portfolio selection, that seeks to allocate a predetermined capital (wealth) over one or multiple periods between assets and stocks in such a way that the wealth of investor (portfolio owner) is maximized and, Simultaneously, its risk minimized. In the paper, we first propose a mathematical Programming model for Portfolio selection to maximize the minimum amount of Sharpe ratios of the portfolio in all periods (max-min problem). Then, due to the uncertain property of the input parameters of such a problem, a Robust Possibilistic Programming model (based on necessity theory) has been developed, which is capable of adjusting the Robust degree of output decisions to the uncertainty of the parameters. The proposed model was tested on 27 companies active in the Tehran stock market. In the end, the results of the model demonstrated the good performance of the Robust Possibilistic Programming model.

The purpose of this study is to develop a dairy global supply chain planning model in which operational and financial dimensions are appropriately integrated in order to adjust the credit sale strategy. In order to evaluate the financial performance of the dairy supply chain, economic value-added index and some financial ratios are used. The proposed model is compared to traditional approaches, which usually use the profit maximization as an objective function. Also, the amount of credit sales is considered as a decision variable for the first time in this research. The developed model utilized a new risk measure, i. e., the fuzzy CVaR, to cope with the uncertainty of the exchange rate and the quality and quantity of returned products. The effectiveness and efficiency of the proposed model are analyzed and assessed using the data of a real dairy supply chain. The analysis of results obtained from the developed fuzzy mathematical model shows an increase in profit and a reduction in semi-variance compared to previously developed models. Also, some numerical experiments analyses index and the impact of credit sales strategy.

The provision of an efficient master plan which is able to integrate the procurement, production and distribution plans is a critical need in the way of achieving the competitive advantage in today’s marketplace. In this paper, a supply chain master planning problem of a drug supply chain is taken into account. The considered drug supply chain includes multiple suppliers, one manufacturer and multiple distribution centers. In this paper, a multi-objective Possibilistic mixed integer linear Programming model (MOPMILP) which minimizes the total logistics cost and maximizes the total value of supplier selection aggregate function is developed. It should be noted that both economic and environmental criteria are considered in the supplier selection objective function to support the green and sustainable purchasing approach. Then to cope with the input parameters tainted with high degree of uncertainty, a new effectual Robust Possibilistic Programming (RPP) model is elaborated. The proposed Robust Possibilistic Programming model is able to appropriately adjust the degree of feasibility and optimality Robustness of output decisions against business-as-usual uncertainty. Also the proposed Robust optimization model can be appropriately applied in the cases in which reliable and sufficient historical data is not available for imprecise parameters (i.e., most of the real-life problems). To show the usefulness and effectiveness of the proposed Robust Possibilistic Programming model numerical and comparative experiments are provided. The numerical results endorse the validity and practicability of the rendered model as well as presenting the efficiency and felicity of the developed approach.

One of the issues of reliable performance in the power grid is the existence of electromechanical oscillations between interconnected generators. The number of generators participating in each electromechanical oscillation mode and the frequency oscillation depends on the structure and function of the power grid. In this paper, to improve the transient nature of the network and damping electromechanical fluctuations, a decentralized Robust adaptive control method based on dynamic Programming has been used to design a stabilizing power system and a complementary static var compensator (SVC) controller. By applying a single line to ground fault in the network, the Robustness of the designed control systems is demonstrated. Also, the simulation results of the method used in this paper are compared with controllers whose parameters are adjusted using the PSO algorithm. The simulation results show the superiority of the decentralized Robust adaptive control method based on dynamic Programming for the stabilizing design of the power system and the complementary SVC controller. The performance of the control method is tested using the IEEE 16-machine, 68-bus, 5-area is verified with time domain simulation.

Considering the cognitive, random, uncertain, and flexible constraints, a Robust, stochastic, Possibilistic, and flexible chance-constrained model was developed based on credibility measurement. The ultimate aim was closedloop supply chain network design. Different attitudes of decision-makers were answered by more flexible measurements of optimistic and pessimistic parameters in the form of credibility measurement. The model has been able to reduce the possible deviation, stochastic deviations, nonfulfillment of demand and capacity constraints, and violation of flexible constraints, which simultaneously include cognitive and random uncertainties and flexibility of constraints in the model. To apply the model, a case study was conducted to design the closed-loop supply chain network of multi-product and multi-period stone paper. The results of implementing the model showed that in different situations and according to the importance of decision makers' opinions, using the range of optimism and pessimism, the number, location of facilities, optimal flow of products and materials between centers in the stone paper supply chain network can be determined. The proposed model was evaluated using Robustness and sensitivity analysis, and its performance was evaluated using nominal data in the realization model, which results showed the appropriate performance of the model.

In recent years, the complexity of the environment, the intense competition of organizations, the pressure of governments on producers to manage waste products, environmental pressures and most importantly, the benefits of recycling products have added to the importance of designing a closed loop supply chain network. Also, the existence of inherent uncertainties in the input parameters is another important factor that the lack of attention them can affect the strategic, tactical and operational decisions of organizations. Given these reasons, this research aims to design a multi-product and multi period closed loop supply chain network model in uncertainty conditions. To this aim, first a mixed-integer linear Programming model is proposed to minimize supply chain costs. Then, for considering model hybrid uncertainties effectively, randomness and epistemic uncertainty, a novel Robust stochastic-Possibilistic Programming (RSPP) approach is proposed. Furthermore, several varieties of RSPP models are developed and their differences, weaknesses, strengths and the most suitable conditions for being used are discussed. Finally, usefulness and applicability of the RSPP model are tested via the real case study in an edible oil industry.

Purpose: This paper aimed to design a Robust blood supply chain model that includes the stages of collection, processing and distribution of blood and blood products taking into account the lifespan and age of demand, which seeks to reduce supply chain costs and reduce the shortage and waste of blood products.Methodology: In this paper, MINLP method is used to model the research problem and in order to face the uncertainty in the problem parameters, the MPFRP method based on fuzzy data is proposed. The designed model was first evaluated for validation with numerical examples in small and large size and using real data in a case study in GAMS software?Findings: Using numerical examples and real data, the output indicates the performance of the proposed model. The output also had acceptable flexibility in the face of uncertainty in the parameters of the research model.Originality/Value: In this study, in order to reduce the shortage of blood products in situations where blood product is not available in the same group as the requested blood product, the ABO-RH adaptability principle has been used to replace the received demand with a compatible inventory. And also, to deal with uncertainty in uncertain parameters in the supply chain a solution based onmixed Possibilistic-flexible Robust Programming is proposed.

Nowadays, the design of a strategic supply chain network under disruption is one of the most important priorities of the governments. One of the strategic purposes of managers is to supply the sustainable agricultural products and food in stable conditions which require the production of soil nutrients. In this regard, some disruptions such as sanctions and natural disasters have a destructive effect on the supply of raw materials and the uncertainty of input parameters plays an undesirable impact on the decision-making levels including strategic, tactical, and operational levels. The present study introduced a new model of resilient supply chain network which was compatible with the realities of the structure of the supply chain for fertilizer in Iran. Notably, the effectiveness of the designed system was promoted by the dominant strategies of reliability. Further, a new Robust Possibilistic approach was proposed which guaranteed the optimality and feasibility Robustness through the efficient solution to deal with the parametric uncertainty. Finally, the results showed that the proposed new Robust Possibilistic combination promoted the optimality Robustness and its effectiveness using an optimal average cost and minimum standard deviation.