LEARNING GENETIC ALGORITHM-BASED MULTI-TASK AND MULTI-RESOURCE ROLLING DISTRIBUTION METHOD

摘要

A learning genetic algorithm-based multi-task and multi-resource rolling distribution method. According to the method, on the basis of a rolling time-domain control principle, a multi-task and multi-resource dynamic rolling distribution mechanism is constructed, comprising determining elements such as a prediction window, a rolling window, a distribution sub-problem, and a rolling mechanism. Task information is updated in real time by means of a current prediction window, and a current rolling window is determined on the basis of the prediction window. The distribution sub-problem is a local distribution problem constructed according to the current rolling window at each planning moment. The rolling mechanism is used for determining an execution position where a distribution scheme ends after the distribution sub-problem is solved and a next planning moment. By means of the rolling distribution mechanism, a complex dynamic distribution problem is converted into rolling updated static distribution problems. According to the method, by means of rolling dynamic planning, a complex dynamic distribution problem is decomposed into a plurality of simple and static scheduling problems, and then the optimal solutions of the sub-problems are combined to replace the optimal solution of the original problem, thereby reducing the solving difficulty of the original problem.