As the number of space objects(SO)increases,collision avoidance problem in the rendezvous tasks or reconstellation of satellites with SO has been paid more attention,and the dangerous area of a possible collision shou...As the number of space objects(SO)increases,collision avoidance problem in the rendezvous tasks or reconstellation of satellites with SO has been paid more attention,and the dangerous area of a possible collision should be derived.In this paper,a maneuvering method is proposed for avoiding collision with a space debris object in the phasing orbit of the initial optimal solution.Accordingly,based on the plane of eccentricity vector components,relevant dangerous area which is bounded by two parallel lines is formulated.The axises of eccentricity vector system pass through the end of eccentricity vector of phasing orbit in the optimal solution,and orientation of axis depends on the latitude argument where a collision will occur.The dangerous area is represented especially with the graphical dialogue,and it allows to find a compromise between the SO avoiding and the fuel consumption reduction.The proposed method to solve the collision avoidance problem provides simplicity to calculate rendezvous maneuvers,and possibility to avoid collisions from several collisions or from“slow”collisions in a phasing orbit,when the protected spacecraft and the object fly dangerously close to each other for a long period.展开更多
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 no...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.展开更多
This paper presents a parallel composite local search algorithm based on multiple search neighborhoods to solve a special kind of timetable problem. The new algorithm can also effectively solve those problems that can...This paper presents a parallel composite local search algorithm based on multiple search neighborhoods to solve a special kind of timetable problem. The new algorithm can also effectively solve those problems that can be solved by general local search algorithms. Experimental results show that the new algorithm can generate better solutions than general local search algorithms.展开更多
基金This work is supported and funded by NSFC(Natural Science Foundation of China)[No.51905272].
文摘As the number of space objects(SO)increases,collision avoidance problem in the rendezvous tasks or reconstellation of satellites with SO has been paid more attention,and the dangerous area of a possible collision should be derived.In this paper,a maneuvering method is proposed for avoiding collision with a space debris object in the phasing orbit of the initial optimal solution.Accordingly,based on the plane of eccentricity vector components,relevant dangerous area which is bounded by two parallel lines is formulated.The axises of eccentricity vector system pass through the end of eccentricity vector of phasing orbit in the optimal solution,and orientation of axis depends on the latitude argument where a collision will occur.The dangerous area is represented especially with the graphical dialogue,and it allows to find a compromise between the SO avoiding and the fuel consumption reduction.The proposed method to solve the collision avoidance problem provides simplicity to calculate rendezvous maneuvers,and possibility to avoid collisions from several collisions or from“slow”collisions in a phasing orbit,when the protected spacecraft and the object fly dangerously close to each other for a long period.
基金supported by the National Natural Science Foundation of China (7060103570801062)
文摘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.
基金supported by the National Natural Science Foundation of China(61473147)the Funding of Jiangsu Innovation Program for Graduated Education(KYLX16-0376)the Funding for Outstanding Doctoral Dissertation in NUAA(BCXJ16-02)
文摘This paper presents a parallel composite local search algorithm based on multiple search neighborhoods to solve a special kind of timetable problem. The new algorithm can also effectively solve those problems that can be solved by general local search algorithms. Experimental results show that the new algorithm can generate better solutions than general local search algorithms.
