Multi-target scheduling method based on fireworks algorithm and genetic algorithm

Abstract

The invention discloses a multi-target scheduling method based on a fireworks algorithm and a genetic algorithm. The multi-target scheduling method is characterized by comprising the steps of setting initial parameters; generating an initial population, and starting iteration by taking the initial population as a current population; calculating a non-dominated solution of the current population, judging whether a new non-dominated solution is generated or not; and if yes, inputting the new non-dominated solution into the optimal solution set; if not, judging whether a set total number of iterations is reached, and if yes, outputting an optimal solution set; if not, calculating the firework scale to obtain a firework group; performing firework explosion operation and Gaussian mutation operation on the firework group; carrying out genetic selection; selecting fireworks; performing population crossover operation; performing population variation operation, and continuing iteration by taking the population subjected to the population variation operation as a current population; the method has the advantages that the genetic algorithm and the fireworks algorithm are combined, the convergence speed is high, the solving precision is high, and therefore the scheduling efficiency and the scheduling precision under the multi-variety small-batch production mode are effectively improved.

Description

technical field [0001] The invention relates to the technical field of production scheduling, in particular to a multi-objective scheduling method based on a fireworks algorithm and a genetic algorithm. Background technique [0002] With the increasing diversification of customer needs and the increasingly fierce market competition, the production method of multi-variety and small batches has gradually become popular; multi-variety and small batches refer to a variety of commodities that need to be produced within a certain construction period, and each commodity type needs to be produced. The number of products is small; the production method of multi-variety and small batch is more complicated, and the production scheduling is also very difficult, which brings great challenges to the production scheduling. [0003] In multi-objective scheduling, due to the complex and changeable production requirements in the multi-variety and small-batch production mode, the production de...

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Problems solved by technology

[0002] With the increasing diversification of customer needs and the increasingly fierce market competition, the production method of multi-variety and small batches has gradually become popular; The quantity is small; the complexity of the multi-variety and small-batch production method is high, and the production scheduling is also very difficult, which brings great challenges to production scheduling

[0003] In multi-objective scheduling, due to the complex and changeable production demand in the multi-variety and small-batch production mode, it often occurs that the production demand exceeds the load of the production equipment. Therefore, it is necessary to find the production plan with the optimal multiple indicators. Only the production plan with the optimal single index can be obtained, and the production plan with the optimal multiple indexes cannot be effectively obtained; and when the existing multi-objective scheduling method solves the complex multi-objective scheduling problem, the obtained Pare The optimal solution set is not comprehensive enough, that is, the solution accuracy is not high enough

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