Generalized multiple time windows model based parallel machine scheduling for TDRSS

The scheduling efficiency of the tracking and data relay satellite system（TDRSS）is strictly limited by the scheduling degrees of freedom（DoF）,including time DoF defined by jobs＇ flexible time windows and spatial DoF brought by multiple servable tracking and data relay satellites（TDRSs）.In this paper,ageneralized multiple time windows（GMTW）model is proposed to fully exploit the time and spatial DoF.Then,the improvements of service capability and job-completion probability based on the GMTW are theoretically proved.Further,an asymmetric path-relinking（APR）based heuristic job scheduling framework is presented to maximize the usage of DoF provided by the GMTW.Simulation results show that by using our proposal 11%improvement of average jobcompletion probability can be obtained.Meanwhile,the computing time of the time-to-target can be shorten to 1/9 of the GRASP.

Polarization-insensitive complementary metamaterial structure based on graphene for independently tuning multiple transparency windows

Polarization-insensitive multiple transparency windows are obtained with a graphene-based complementary metamaterial structure in terahertz regions,which is composed of two kinds of monolayer graphene perforated in shapes of a cross and four identical split rings that construct a resonator.The geometric parameters of resonators are different from each other.Numerical and theoretical results show that the quantum effect of Autler-Townes splitting is the key factor for appearance of transparency windows within the resonant dips.Further investigation demonstrates that by employing the fourfold-symmetry graphene complementary structure,polarization-independent transparency windows can be achieved.Moreover,multiple transparency windows can be separately manipulated over a broad frequency range via adjusting the chemical potential of the corresponding graphene resonators,and the bandwidth as well as resonance strength can also be tuned by changing the relative displacement between resonators each consisting of a cross and four split rings.The proposed metamaterial structure may be utilized in some practical applications with requirements of no polarization-varied loss and slowing the light speed.

Self-adaptive large neighborhood search algorithm for parallel machine scheduling problems

A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely not all jobs can be scheduled within specified scheduling horizons due to the limited machine capacity. The objective is thus to maximize the overall profits of processed jobs while respecting machine constraints. A first-in- first-out heuristic is applied to find an initial solution, and then a large neighborhood search procedure is employed to relax and re- optimize cumbersome solutions. A machine learning mechanism is also introduced to converge on the most efficient neighborhoods for the problem. Extensive computational results are presented based on data from an application involving the daily observation scheduling of a fleet of earth observing satellites. The method rapidly solves most problem instances to optimal or near optimal and shows a robust performance in sensitive analysis.

Recursive Least Squares Estimator with Multiple Exponential Windows in Vector Autoregression

In the parameter tracking of time-varying systems, the ordinary method is weighted least squares with the rectangular window or the exponential window. In this paper we propose a new kind of sliding window called the multiple exponential window, and then use it to fit time-varying Gaussian vector autoregressive models. The asymptotic bias and covariance of the estimator of the parameter for time-invariant models are also derived. Simulation results show that the multiple exponential windows have better parameter tracking effect than rectangular windows and exponential ones.