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 most important issues in disaster response phase is to supply the relief items which is needed by affected areas. The uncertainty of this demand causes many problems. This paper presents a novel Robust Possibilistic programming model for a routing and scheduling problem in a relief commodities distribution network under demand uncertainty. In relief commodities distribution operations, the possibility of servicing each affected area by multiple vehicles and time window constraint have been considered. The objective of the proposed model is to reduce the total time required by the relief vehicles to reach the affected areas. The fourth region of Tehran city as a case study is provided to illustrate the performance and applicability of the proposed model. Finally, to assess the Robustness of the solutions obtained by the novel Robust optimization model, they are compared to those generated by the deterministic mixed-integer linear programming model in a number of realizations under different test problems.

Today, the high importance of achieving food security for communities has made it necessary to provide a new perspective on the design of the grain supply chain distribution network to better adapt to real-world uncertainties and also to take into account disruptions. To this end, in this study, a mixed-integer linear programming model has been developed for the distribution network design problem in the grain supply chain, which has three objectives such as minimizing costs and maximizing job opportunities, both of which are examples of sustainability. In addition, considering the importance of grain in the food basket of households and the importance of food security, the third objective function has been developed focusing on the issue of resilience to deal with any disruption in the design of the distribution network. The Robust-Possibilistic programming approach is used to deal with uncertain demand and the multi-objective problem is solved using the improved epsilon constraint method. Finally, a compromised solution is selected using the TOPSIS method.

Design of an appropriate Cellular Manufacturing System (CMS) leads to system exibility and production e ciency by using the similarities in the manufacturing process of products. One of the main issues in these systems is to consider product quality level and worker's skill level in the production process. This study proposes a comprehensive bi-objective Possibilistic nonlinear mixed-integer programming model under uncertain environment to design a suitable CMS with the aim of minimizing the total costs and total inaction of workers and machines, simultaneously. In this respect, the demand for each product with a speci c quality level and linguistic parameters such as product quality level, worker's skill level, and job hardness level on machines are considered under fuzzy environment. To this end, the Robust Possibilistic programming approach is tailored to cope with fuzzy impute parameters. Finally, a real case study is provided to show the e ciency and applicability of the proposed model. In this respect, the proposed approach could reduce the total costs by 23. 6% and the total inaction of workers and machines by 11. 7% in comparison with real practice. In addition, the performance of the presented model is demonstrated by comparing the results obtained from the proposed model and actual practice.

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.