Method and system for production scheduling based on improved particle swarm optimization and heuristic strategy

Abstract

The invention discloses a method and a system for production scheduling based on improved particle swarm optimization and a heuristic strategy. The method comprises the following steps: proposing constraint conditions; defining an objective function, namely, a fitness function; carrying out particle swarm initialization; randomly initializing swarm particles, including position and velocity; carrying out iterative optimization: updating the velocity and position of particles, and finding out an optimal solution; setting the maximum number of iterations as an iteration termination condition, judging whether the iteration termination condition is satisfied, and outputting a global optimal solution if the iteration termination condition is satisfied; if iteration termination condition is not satisfied, jumping to the previous step to continue to find out an optimal solution. The beneficial effects are as follows: the computing speed of the algorithm is increased by using a novel particle encoding method; the problem of local optimum in the process of the traditional particle swarm optimization algorithm can be solved; the phenomenon of infeasible solutions is avoided to a great extent; and efficient and optimal production scheduling is realized.

Description

technical field [0001] The invention belongs to the application field of production scheduling in the discrete manufacturing industry, and in particular relates to a production scheduling method and system based on improved particle swarm and heuristic strategies. Background technique [0002] The discrete manufacturing industry is gradually transforming from the traditional single-species, mass production mode to the "order-oriented" multi-species, small-batch production mode, and the following phenomena still commonly exist in the production process: A. Production planning depends on manual experience, without scientific basis. B. The phenomenon of inserting orders in the actual production process affects the production progress. C. With the progress of the production process, it is impossible to evaluate the remaining capacity in a timely and effective manner. D. There is a serious delay in delivery. [0003] The above-mentioned problems in the production process lead ...

AI Technical Summary

Problems solved by technology

However, the traditional particle swarm optimization algorithm still has the following shortcomings and deficiencies: A. When the particle swarm optimization algorithm converges too fast in the early stage, it is easy to fall into the local optimum in the later stage of the algorithm operation, and the global optimal solution cannot be obtained

B. The position movement of particles with the change of the production process will produce invalid particles, resulting in no solution

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