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.

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.

The present study sustainable management of Guilan Saravan Sylvan Park was planned. The used method in the form of decision support model in three10-year period was studied for parks of various states and with considering the economic, ecological and social criteria during the years 2008 to 2038. By using hierarchical fuzzy analysis method, the reaching to an optimal level of economic, ecological and social simultaneously and as their combined utility values in various periods, decisions and different states of park is obtained. The results showed that in order to reach to an optimal level of purposes simultaneously, at the beginning of the first 10-year period implementation of ecological decision, at the beginning of the second 10 years period in three considered states for the Sylvan Park implementation of social, ecological and ecological decisions give the most combined utility value of purposes respectively. Results of Stochastic Dynamic Programming showed that implementing the ecological decisions at the beginning of the first 10-year period, implementing the ecological decision by considering the second designed state of park at the beginning of the second 10-year period, and by applying the first designed state of park at the beginning of third 10-year-old period, the implementation of social decisions is desirable to achieve sustainable management in Saravan Sylvan Park. According to the findings, the implementation of these determined decisions can be useful and maximum of desirable use from Park potentials in the economic, social - recreational and conservation and sustainability of indigenous species problems, and reposit the park to the next generation with the best condition.

In literature about inflation targeting، main factor is interest rate. In new surveys، price of some assets as housing is important in inflation targeting. Iran inflation targeting is based on money changes and is not based on interest rate and housing price. In this paper، we want to survey problems of wrong policy of Iran policy makers. We reveal that، using rate of deposit profit and growth of housing price in two separate models for inflation targeting have same results. Results of first model shows that policy makers in Iran for determination of interest rate do not attention to economic conditions، inflation and production gap. Therefore inflation and production gap have small response to interest rate changes.

In the last decades, various methods have been proposed for Stochastic operation optimization of reservoirs out of which one is the Stochastic Dual Dynamic Programming (SDDP). Reviewing the literature shows that in most previous studies the inflow uncertainties have been mostly modeled by auto-regressive AR (1) or seasonal aut-regressive PAR (2) models when using SDDP. Another approach toward inflow uncertainty modeling by SDDP uses Markov Chain which has received less attention. Furthermore, applicability of SDDP in developing long-term operation policies has not been tested before. In this study, MSDDP model has been developed which uses Markov chain concept. Also, after presenting an algorithm for using SDDP and MSDDP for formulating long-term operation policies, their performances have been compared in Karoun multi-reservoir hydropower system. The results have shown absolute superiority of MSDDP in formulating long-term operation policies. Furthermore, operation policies obtained from SDDP have shown highest compatibility with the best possible operation policy when there is no uncertainty (deterministic conditions).

An appropriate facility layout is required to reduce total manufacturing cost, especially in uncertain environments. The design of a desirable facility layout isessential when the rearrangement of the facilities is expensive. Using RoutingFlexibility (RF) as a principle of the Flexible Manufacturing System (FMS) canlead to the fulfillment of this need. This paper propounds two new mathematical models for the Dynamic Facility Layout Problem (DFLP) with Stochasticapproaches. The RF is considered when the demands of the independent partsfollow Exponential and Normal distributions in which their parameters randomlyalter from period to period. The primary nonlinear models are first linearized by the proposed innovative technique. Then, the performance of the proposed models andthe linearization technique is assessed by solving two test problems. Next, the RFeffect on the manufacturing system is analyzed. The obtained results verify thevalidity and applicability of the proposed models. It is also shown that the suggestedlinearization technique is an efficient technique with 99% accuracy, even ifconvexity conditions are not met.

This study aimed at presenting a method for formulating optimal production, repair and replacement policies. The system was based on the production rate of defective parts and machine repairs and then was set up to optimize maintenance activities and related costs. The machine is either repaired or replaced. The machine is changed completely in the replacement process, but the production rate of defective parts decreases in the repair process. The repair time and number of repairs will affect this process. The aim of this study is to find decision variables that minimize the final cost of repair, replacement, maintenance and prevention of failures in a given time horizon. As a case study, the variables were evaluated at Arak Pishgam Company to achieve optimal conditions. The proposed model was developed based on the semi-Markov decision process (SMDP). In addition, Stochastic Dynamic Programming model was used to achieve optimal conditions.

In this study, regarding to special condition of Sistan region, Stochastic Helmand River flow in border of Iran and direct relation between farmer’s income and cost with water flow of Chahnimeh reservoir, to allocate water Stochastic Dynamic Programming and markov chain were used. For this reason first data series were gathered for 20 (1999-2009) years. Then a good model was made to show the region condition. Finally, regarding to different states of system in farming season and their probabilities, water allocate optimally between Sistan, Zahak and Miankangi and cropping area were determined. Results show that using Stochastic Dynamic Programming reservoir ‘water was optimally managed, water demand was determined in each area and cropping area was estimated. Therefore, this model is suggested to Chahnimeh Reservoir management.

In this paper, we use both the Stochastic Dynamic Programming and Bayesian inferences concepts to design an optimum-acceptance-sampling-plan policy in quality control environments. To determine the optimum policy we employ a combination of costs and risk functions in the objective function. Unlike previous studies, accepting or rejecting a batch are directly included in the action space of the proposed Dynamic Programming model. Using the posterior probability of the batch to be in state p (the probability of non-conforming products), first we formulate the problem into a Stochastic Dynamic Programming model. Then, we derive some properties for the optimal value of the objective function, which enable us to search for the optimal policy that minimizes the ratio of the total discounted system cost to the discounted system correct choice probability.