two-stage stochastic programming

In this paper, a novel two-stage multi-period stochastic model is developed to obtain a comprehensive plan. This plan aims to manage the assets and liabilities such that all legal and budget constraints are satisfied. Assets in the model include short- and long-term loans with reasonable interest rates, investments in the stock market, varied bonds with different expirations, investments in other banks, and the legal budget in the Central Bank. However, liabilities encompass all types of sight and investment deposits with different maturities. In the model, each type of deposit's amount is considered a decision variable, while its total amount is assumed to be stochastic. The mathematical model is constructed in an innovative way such that all previous loans and bonds with possible transactions in the planning horizon could be considered initial parameters. Real data for a commercial bank in Tehran, the Islamic Republic of Iran capital, are used to construct and check the optimization model. The total revenues obtained through the mathematical model and one achieved based on the experiences of financial experts in the commercial bank for four years are compared.

Suppliers as one of the main sources of vulnerability may lead to disruption and risk in supply chains. Thus, resilient supplier selection can lead to an increase in the resilience of the supply process, especially in automotive supply chains. The goal of this study is to select a set of resilient suppliers and optimal demand allocation in an automotive supply chain under risk. For this purpose, a bi-objective two-stage stochastic programming model is presented. In contrast to previous mathematical models, our model includes a new objective function to consider the supplier’s delivery performance as one of the criteria of resilient supplier selection and also the k-means clustering method is used to cluster and decrease the number of disruption scenarios. In the proposed model, due to the uncertainty of demand, chance-constrained programming approach has been utilized. The augmented Ɛ-constraint method is implemented to solve the presented model. Finally, sensitivity analysis has been done to determine the effect of parameter changes on the final results. The results of the research indicate that contingency planning can reduce the effect of disruption risks. The findings also show that the strategy of the supply chain regionalization is important in reducing the effects of environmental disruption.