American Institute of Mathematical Sciences

In this paper, we consider the new online scheduling model with linear lookahead intervals, which has the character that at any time \begin{document}$ t $\end{document}, one can foresee the jobs that will coming in the time interval \begin{document}$ (t, \lambda t+\beta ] $\end{document} with \begin{document}$ \lambda\geq1, \beta\geq 0 $\end{document}. We consider online scheduling of unit length jobs on \begin{document}$ m $\end{document} identical parallel-batch machines under this new lookahead model to minimize the maximum flowtime and the makespan, respectively. We give some lower bounds for these problems with the batch capacity \begin{document}$ b = \infty $\end{document} and \begin{document}$ b<\infty $\end{document}, respectively. And for the bounded model to minimize makespan, we give an online algorithm with competitive ratio \begin{document}$ 1+\alpha $\end{document} for \begin{document}$ 1\leq \lambda <4/3, 0\leq \beta\leq \frac{4-3\lambda}{6} $\end{document} and \begin{document}$ \frac{3}{2} $\end{document} for \begin{document}$ \lambda\geq1, 0\leq\beta<1 $\end{document}, where \begin{document}$ \alpha $\end{document} is the positive root of \begin{document}$ \lambda\alpha^2+(\lambda+\beta)\alpha+\beta-1 = 0 $\end{document}. The online algorithm is best possible when \begin{document}$ 1\leq \lambda <4/3, 0\leq \beta\leq \frac{4-3\lambda}{6} $\end{document}.

We present an effective hybrid metaheuristic of integrating reinforcement learning with a tabu-search (RLTS) algorithm for solving the max–mean dispersion problem. The innovative element is to design using a knowledge strategy from the \begin{document}$ Q $\end{document}-learning mechanism to locate promising regions when the tabu search is stuck in a local optimum. Computational experiments on extensive benchmarks show that the RLTS performs much better than state-of-the-art algorithms in the literature. From a total of 100 benchmark instances, in 60 of them, which ranged from 500 to 1, 000, our proposed algorithm matched the currently best lower bounds for all instances. For the remaining 40 instances, the algorithm matched or outperformed. Furthermore, additional support was applied to present the effectiveness of the combined RL technique. The analysis sheds light on the effectiveness of the proposed RLTS algorithm.

Heating, Ventilation, and Air-Condition (HVAC) systems are considered to be one of the essential applications for modern human life comfort. Due to global warming and population growth, the demand for such HVAC applications will continue to increase, especially in arid areas countries like the Arabian Gulf region. HVAC systems' energy consumption is very high and accounts for up to 70% of the total load consumption in some rapidly growing GCC countries such as Qatar. Additionally, the local extremely hot weather conditions usually lead to typical power demand peak issues that require adequate mitigation measures to ensure grid stability. In this paper, a novel control scheme for a combined group of Air-Conditioners is proposed as a peak-shaving strategy to address high power demand issues for Photo-Voltaic(PV)-integrated micro-grid applications. Using the local daily ambient temperature as input, the AC group control optimization is formulated as a Mixed-Integer Quadratic Programming (MIQP) problem. Under an acceptable range of indoor temperatures, the units in the same AC group are coordinately controlled to generate desired power consumption performance that is capable of shaving load peaks for both power consumption and PV generation. Finally, various simulations are performed that demonstrate the effectiveness of the proposed control strategy.

Considering the parameter uncertainty and actuator failure of hypersonic vehicle during maneuvering, this paper proposes a state observer-based hypersonic vehicle fault-tolerant control (FTC) system design method. Because hypersonic vehicles are prone to failure during maneuvering, the state quantity cannot be measured. First, a state observer-based FTC control method is designed for the linear parameter-varying (LPV) model with parameter uncertainty and partial failure of the actuator. Then, the Lyapunov function is used to demonstrate the asymptotic stability of the closed-loop system. The performance index function proved that the system has robust stability under the disturbance condition. Subsequently, the linear matrix inequality (LMI) was used to solve the observer parameters and the corresponding gain matrix in the control system. The simulation results indicated that the designed controller can track the flight command signal stably and has strong robustness, which verified the effectiveness of the design controller.