Computational grids have provided the usage of computational distributed resources for computation-intensive applications. The development of programs that use these capabilities is one of the challenging issues for grid computing. In this article, an effort has been made in order to solve this problem by presenting mobile-agent-based parallel programming on the grid. The presentation of this model, which has been materialized by extending Alchemi™ grid infrastructure, adding agent properties and navigational commands that let the user to develop his/her program by using agents’ mobility and communication between them. In order to evaluate the system, algorithm of matrix multiplication as well as algorithm of finding the convex hull of a series of points has been implemented in the mentioned system.

Appropriate scheduling and sequencing of tasks on machines is one of the basic and significant problems that a shop or a factory manager encounters; this is why in recent decades extensive studies have been done on scheduling issues. One type of scheduling problems is justin-time (JIT) scheduling and in this area, motivated by JIT manufacturing, this study investigates a mathematical model for appraising a multi-objective programing that minimize total weighted tardiness, earliness and total flowtime with fuzzy parameters on parallel machines, simultaneously with respect to the impact of machine deterioration. Besides, in this paper attempted to present a defuzzification approach and a heuristic method based on genetic algorithm (GA) to solve the proposed model. Finally, several dominant properties of optimal solutions are demonstrated in comparison with the results of a state-of-the-art commercial solver and the simulated annealing method that is followed by illustrating some instances for indicating validity and efficiency of the method.

Appropriate scheduling and sequencing of tasks on machines is one of the basic and significant problems that a shop or a factory manager encounters; this is why in recent decades extensive researches have been done on scheduling issues. A type of scheduling problems is justin-time (JIT) scheduling and in this area, motivated by JIT manufacturing, this study investigates a mathematical model for appraising a multi-objective programing that minimize total weighted tardiness, earliness and total flow time with fuzzy parameters on parallel machines, simultaneously with respect to the impact of machine deterioration. Besides, in this paper, attempts are made to present a defuzzification approach and a heuristic method based genetic algorithm (GA) to solve the proposed model. Finally, several dominance properties of optimal solutions are demonstrated in comparison with the results of a state-of-the-art commercial solver and the simulated annealing method that is followed by illustrating some instances for indicating validity and efficiency of the method.

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.

In this paper, an offline energy management system (EMS) is proposed for parallel hybrid electric vehicles (HEVs). The proper energy management system is necessary for dividing torque between electrical motor and Internal Combustion Engine (ICE). The battery is a crucial component of hybrid electric vehicles and affects significantly the cost and the performance of the whole vehicle. The primary factors accelerating battery aging are high temperatures and high states of charge (SOC) of the battery. SOC is the most important state variable in EMS, and usually considered as the only dynamic variable in past researches, but the battery temperature is often considered to be constant for simplicity and the effects of EMS on the temperature variations are neglected. In this paper, first, dynamic programming is applied to a parallel HEV without considering variation of the temperature of the battery. Then, the model of battery is improved by modelling the cooling system to take into account temperature variations and show how neglecting thermal dynamics of the battery in EMS is impractical. Finally, by integrating the battery temperature as a state variable in the optimization problem, a new energy management strategy controlling variations of the battery temperature and SOC is proposed. The simulation results on tested vehicle show that in the proposed method charge and temperature of the battery is controlled so that the proposed EMS method prevents uncontrolled variations of the battery temperature and reduces the degradation rate of it.

The objective of this study is to verify the importance of the capabilities of cloud computing services in managing and analyzing big data in business organizations because the rapid development in the use of information technology in general and network technology in particular, has led to the trend of many organizations to make their applications available for use via electronic platforms hosted by various Companies on their servers or so-called cloud computing that have become an excellent opportunity to provide services efficiently and at low cost, but managing big data presents a definite challenge in the cloud space beginning with the processes of extracting, processing data, storing data and analyze it. Through this study, we dealt with the concept of cloud computing and its capabilities in business organizations. We also interpreted the notion of big data and its distinct characteristics and sources. Finally, the relationship between cloud computing with big data was also explained (extraction, storage, analysis).

This paper presents a new mixed-integer goal programming (MIGP) model for a parallel machine scheduling problem with sequence-dependent setup times and release dates. Two objectives are considered in the model to minimize the total weighted flow time and the total weighted tardiness simultaneously. Due to the complexity of the above model and uncertainty involved in real-world scheduling problems, it is sometimes unrealistic or even impossible to acquire exact input data. Hence, we consider the parallel-machine scheduling problem with sequence-dependent set-up times under the hypothesis of fuzzy processing time's knowledge and two fuzzy objectives as the MIGP model. In addition, a quite effective and applicable methodology for solving the above fuzzy model is presented. At the end, the effectiveness of the proposed model and the denoted methodology is demonstrated through some test problems.