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.

Computational dynamic programming, while of some use for situations typically encountered in industrial and systems engineering, has proved to be of much greater significance in many areas of computer science. We review some of these applications here.

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.

In recent years, supplier selection (SS) has been one of the fundamental issues in the supply chain(SC). Sustainable suppliers are selected based on the quantitative and qualitative evaluation of economic,environmental, and social studies. This paper examines sustainable SS and order allocation (OA) for a singleproduct in a multistage setting. To model the problem, in the first step, the best-worst method (BWM) is usedto determine the weights of the sustainability criteria. Then, given the uncertainty of personal judgment,evidential reasoning (ER) is used to evaluate suppliers. Suppliers are also compared based on the minimumand maximum utility function in case of lack of information. In the next step, a model is designed to optimizesuppliers' total purchase value (TVP) and optimize total purchase cost (TCP). Demand is assumed to berandom. This assumption leads to a set of scenarios based on the time horizon. To solve the model, a dynamicprogramming (DP) is presented. Finally, a case study is given to detail the methodology.

NP-hard optimization problems are computationally challenging tasks that require significant resources to solve, particularly as problem sizes increase. In this paper, we introduce a novel framework, GiraDP, which converts dynamic programming problems into graphs and solves these large-scale graphs efficiently. GiraDP addresses NP-hard optimization problems by leveraging advanced graph processing techniques. The framework utilizes Giraph and Hadoop in its architecture to manage extensive datasets and complex computations. It generates input data for Giraph and identifies active vertices that correspond to sub-problems within the larger problem. Our experiments include scenarios with 20, 40, and 60 items, across varying knapsack capacities. The results indicate that while the serial algorithm performs best at low capacities, it fails to handle larger instances due to memory limitations, resulting in heap space errors. In contrast, GiraDP demonstrates superior efficiency and scalability for high capacities and large item sets. The MPI-based approach also shows improved performance over the serial algorithm for larger problems, although it does not match the efficiency of GiraDP. These findings underscore the importance of distributed computing solutions for large-scale optimization problems

Portfolio selection has always been one of the important issues in the field of investment management, which discusses how to allocate an investor's capital to different assets and form an efficient portfolio. If the modeling assumptions for portfolio optimization is closer to the real world, the results will be more reliable. Considering single horizon for investment is not real and more investors are investing for more than one period to be able to revise their positions over time. Moreover, in the real world, data and parameters are always uncertain. Therefore, the development of multi-period portfolio optimization models is a basic requirement. In this paper, based on the portfolio theory, a new multi-period portfolio selection model is proposed, which contains transaction costs, liquidity constraints, threshold constraints, cardinality constraints and class constraints. Moreover, mean absolute deviation is used as a measure of risk and uncertainty of data is modeled with scenario tree. Also, in order to solve the proposed model, the dynamic programming method has been used and finally, the model efficiency was tested using data for 5 stocks from Tehran Stock Exchange in a period of 1390 to 1394. In the proposed model, the effect of some factors such as boundary of decision variables and the number of assets in the portfolio is examined. The results indicate that the proposed model has a suitable performance and completely consistent with the theory.