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Control method for solving scheduling of operating workshops under complex production environment based on improved genetic algorithm

An improved genetic algorithm and job shop technology, applied in the field of job shop scheduling control, can solve problems such as poor practicability, poor reliability, and poor accuracy of job shop scheduling technology

Inactive Publication Date: 2012-07-25
盐城新咏投资发展有限公司
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Problems solved by technology

[0007] In order to overcome the deficiencies of poor accuracy, poor reliability and poor practicability of the existing job shop scheduling technology, the present invention provides a solution based on improved genetic algorithm with good accuracy, good reliability and strong practicability Job shop scheduling control method in complex production environment

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  • Control method for solving scheduling of operating workshops under complex production environment based on improved genetic algorithm
  • Control method for solving scheduling of operating workshops under complex production environment based on improved genetic algorithm
  • Control method for solving scheduling of operating workshops under complex production environment based on improved genetic algorithm

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Embodiment Construction

[0072] The present invention will be further described below in conjunction with the accompanying drawings.

[0073] refer to Figure 1 to Figure 4 , a method based on an improved genetic algorithm to solve the job shop scheduling control method in a complex production environment, the control method includes the following steps:

[0074] 1. Determine the objective function of fuzzy parameter job shop scheduling

[0075] In order to more closely reflect the situation that job shop scheduling maximizes customer satisfaction and customers have different satisfaction levels for different products, a job shop scheduling model with fuzzy parameters is proposed to maximize all product satisfaction and maximize minimum satisfaction as The objective function is

[0076] z 1 = Σ i = 1 n w i AI ...

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Abstract

The invention relates to a control method for solving scheduling of operating workshops under a complex production environment based on an improved genetic algorithm. The control method comprises the following steps: 1) setting an operating workshop scheduling model with a fuzzification parameter and taking the maximized satisfaction for all products and the maximized minimum satisfaction as a target function; 2) solving the target function by adopting the improved genetic algorithm, (2.1) adopting encoding based on working sequence; (2.2) running a G&T algorithm for several times so as to generate an initial group; (2.3) taking the target function as a fitness function for evaluating an individual; (2.4) performing selecting and interlacing operations; (2.5) performing reversal variation; (2.6) combining all groups and continuing to evolve till converging when the individuals of each group are all converged to a certain degree; and (2.7) taking a preset maximum evolving algebra Nmax as a stopping condition, and taking the present best solution as an optimal solution, thereby obtaining a scheme for solving the scheduling of operating workshops under the complex production environment. The control method provided by the invention has the advantages of excellent accuracy, better reliability and strong practicability.

Description

technical field [0001] The invention relates to the technical field of job shop scheduling control, in particular to a method for solving job shop scheduling control in complex production environments. Background technique [0002] In recent years, the research on JSP (Job-Shop Scheduling Problem, JSP) has broken through the original scope of operations research, and has been widely used in management science, cybernetics, artificial intelligence, industrial engineering, system engineering and other fields. Scholars have carried out fruitful research work with their respective domain knowledge, thus promoting the development and integration of various optimization algorithms. With the development of advanced manufacturing technology, the meaning of the job shop scheduling problem has been expanded, adding attributes such as randomness, dynamicity, uncertainty, constraint, and multi-objectiveness, which is closer to the actual production situation. [0003] Most of the curre...

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Application Information

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IPC IPC(8): G06Q10/06G06N3/12
Inventor 陈勇盛家君邱晓杰吴云翔潘益菁
Owner 盐城新咏投资发展有限公司
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