Finding the location for plans like factories or warehouses for any organization is an important and strategic decision. Costs of transportation which are the main part of the price of the goods, is the function of the location of these projects. to find the optimum location of these projects, there have been various methods proposed which are usually defined (not random). In reality and in dealing with real world conditions, taking into account influential and effective parameters leads to unexpected results. In this research, while introducing these defined algorithms, it been tried to random location model based on the existing models. Regarding this, by studying the models taken in random location we tried to propose an efficient and effective model. For this purpose, by using randomized planning and randomized constrained planning, we turn random model to defined model, which is solvable by using the last algorithm or standard planning methods.

This paper introduces a Stochastic dynamic programming (SDP) model which considers water demand as a state variable. This model called uncertain demand SDP (UDSDP), is a developed form of variable demand-driven advanced models. In this model demand is converted from deterministic to Stochastic form. At first, using available climatic data and methods of determination for evapotranspiration of crops for a region in Isfahan, Iran, the time series of irrigation demand is built. Subsequently, the statistical characteristic of this time series is calculated to incorporate demand uncertainty in optimization model.Optimal policies of UDSDP model are evaluated for Zayandeh-rud riverreservoir system in a real time simulation model. Reliability indices of this model is calculated and compared with the results of a deterministic demand optimization model. This comparison shows the ability and effectiveness of UDSDP model especially when the value of demand uncertainty is significant relative to other uncertainties in reservoir operation model.

Nowadays, the sourcing problem has become more challenging for supply chain members. Dierent types of sourcing for dierent market conditions are presented in the literature. In this paper, an option contract, as an ecient tool for sourcing, is developed in a multi-period setting in which the price and demand follow two Stochastic processes. The sourcing decision is analyzed from a risk neutral and a risk averse decision-maker point of view. This paper applies the Stochastic programming approach to model the presented option contract based on price and demand uncertainties. Next, using CVaR as a coherent risk measure, the eects of risk on sourcing problem are studied. By numerical example, using the presented ecient frontier, the simulation results of our developed models show that the decision maker can make a trade-obetween risk and cost associated with the sourcing problem. The paper also performs a sensitivity analysis in order to demonstrate the eects of change in cost parameters on the results of our option model.

In this research, Stochastic goal programming is applied in water resource management. It is a multi-attribute decision support model which produces satisfactory solutions in uncertainty conditions. The satisfaction of beneficiary users and ecological impacts are investigated in this paper. Also, using sensitivity analysis, the effects of changes of parameters on the model outputs have been investigated. The Stochastic goal programming model has been applied for Booshkan valley in the year 2009. Results show that the proposed model is capable to capture multiple objectives. In addition, having the risk parameters being defined, the decision maker can investigate different scenarios, based on different values for parameters.

In this paper, a Multi-Choice Stochastic Bi-Level programming Problem (MCSBLPP) is considered where all the parameters of constraints are followed by normal distribution. The cost coefficients of the objective functions are multi-choice types. At first, all the probabilistic constraints are transformed into deterministic constraints using Stochastic programming approach. Further, a general transformation technique with the help of binary variables is used to transform the multi-choice type cost coefficients of the objective functions of Decision Makers (DMs). Then the transformed problem is considered as a deterministic multichoice bi-level programming problem. Finally, a numerical example is presented to illustrate the usefulness of the paper.

This paper deals with day-ahead programming under uncertainties in microgrids (MGs). A two-stage Stochastic programming with the fixed recourse approach was adopted. The studied MG was considered in the grid-connected mode with the capability of power exchange with the upstream network. Uncertain electricity market prices, unpredictable load demand, and uncertain wind and solar power values, due to intrinsically Stochastic weather changes, were also considered in the proposed method. To cope with uncertainties, the scenario-based Stochastic approach was utilized, and the reduction of the environmental emissions generated by the power resources was regarded as the second objective, besides the cost of units’ operation. The ɛ-constraint method was employed to deal with the presented multi-objective optimization problem, and the simulations were performed on a sample MG with one month of real data. The results demonstrated the applicability and effectiveness of the proposed techniques in real-world conditions.

This paper presents a scenario-based multistage Stochastic programming model to deal with multi-period portfolio optimization problem with cardinality constraints and proportional transaction costs. The presented model aims to minimize investor's expected regret, while setting a minimum level of expected return. To generate the scenario tree of Stochastic parameters, a random walk model based on Johnson transformation and a sampling procedure is used. To implement the scenario tree generation method, historical returns of 28 domestic indices are used. Then, the scenario tree of Stochastic parameters are used to solve the proposed multistage Stochastic programming model. In addition, the impact of transaction costs, minimum expected returns and predetermined target wealth are investigated. Numerical results show that transaction costs, minimum expected returns and target wealth have a direct impact on expected regret. Finally, back testing simulation is used to assess and analyze the impact of the proposed approach in a dynamic, multi-period setting.