Control method for solving flexible job shop scheduling problem based on genetic algorithm

Abstract

The invention relates to a control method for solving a flexible job shop scheduling problem based on a genetic algorithm, which is divided into six parts such as encoding and decoding, initial population generation, crossover, mutation, fitness calculation and selection. The control method is characterized in that a segmented encoding method is adopted, chromosome encoding is divided into a machine selection part and a procedure selection part, and chromosomes are decoded according to a certain mode so as to acquire corresponding manufacturing procedures and corresponding manufacturing machines; an initial population is generated by adopting a mode of combining various search modes; and fitness calculation aims to solve a problem of how to solve the execution time of certain legal scheduling and judge the quality of the scheduling. The control method provided by the invention not only has great advantages in solving quality, but also has the same excellent performance in improving the solving speed and processing a large-scale flexible job shop scheduling problem.

Description

technical field [0001] The invention relates to a genetic algorithm-based control method for solving the flexible job shop scheduling problem (FJSSP), which belongs to the field of numerical control processing. Background technique [0002] With the advent of the industry 4.0 era, e-commerce continues to prosper globally, customer needs are increasingly personalized, product cycles are shortened, delivery times are shortened, and product categories are increasing. Rapid customization will be a major development trend. . How to accurately estimate the delivery time of the product to the maximum extent and utilize the production materials efficiently has undoubtedly become a very important subject. In order to solve the above problems, it is undoubtedly of great significance to improve the quality of production planning by studying efficient control methods to solve FJSSP. [0003] Due to the characteristics of large-scale, multi-objective, complex constraints, and dynamic u...

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Problems solved by technology

[0003] Due to the characteristics of large-scale, multi-objective, complex constraints, and dynamic uncertainty in process industry production planning and scheduling problems, it is difficult to use conventional modeling and optimization methods directly, and it is difficult to achieve reasonable allocation of resources and optimal sequencing of jobs using manual scheduling methods

The above solutions not only take a long time to solve, but the quality of the solution is not so ideal, and it is even more powerless when dealing with large-scale FJSSP. Therefore, it is necessary to discuss the modeling optimization and effective solution methods suitable for the production planning and scheduling of the process industry become a key theoretical problem to be solved urgently

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