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Improved genetic algorithm for flexible workshop scheduling

A technology for improving genetic algorithm and job shop scheduling, which is applied in the field of flexible job shop scheduling and job shop scheduling, can solve problems such as difficult decoding, easy prematurity, and complex coding methods of genetic algorithms, so as to increase search space, increase diversity, and improve performance effect

Inactive Publication Date: 2017-05-03
SICHUAN YONGLIAN INFORMATION TECH CO LTD
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Problems solved by technology

[0007] The object of the present invention is: first. Solve the problem that the existing genetic algorithm is complicated in encoding mode and difficult to decode; Second. Solve the problem that the search and development ability of the genetic algorithm is weak and easy to mature; Third. Solve the genetic operator operation in the evolution process. infeasible problem

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  • Improved genetic algorithm for flexible workshop scheduling
  • Improved genetic algorithm for flexible workshop scheduling
  • Improved genetic algorithm for flexible workshop scheduling

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Embodiment Construction

[0027] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention and not to limit the present invention.

[0028] The present invention adopts a new encoding method, which is simple and convenient and does not require special decoding. The genetic operator based on process priority protection is used to avoid the generation of infeasible solutions, and the calculation of dynamic variation probability is more in line with natural laws and can control the search direction, improving algorithm performance. .

[0029] The flexible job shop scheduling problem can be formulated as follows: the set of n independent jobs J = {J 1 , J 2 ,...,J n}, set of m devices M={M 1 ,M 2 ,...,M m}, the process cont...

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Abstract

The invention proposes an improved genetic algorithm for flexible workshop scheduling, and the algorithm relates to the technical field of workshop scheduling, and specifically relates to the technical field of flexible workshop scheduling. The invention aims at the problems that a conventional genetic algorithm is complex in coding mode, is difficult for decoding, is weaker in search and development capability and is liable to be mature early and a non-feasible solution is liable to appear in the operation of a genetic operator. Compared with a conventional algorithm, the algorithm has the following improvements that 1, the coding is just performed on one chromosome, a coding chromosome gene consists of a ternary array (i, j, k), the coding mode is simple and convenient, and there is no need of decoding; 2, a positioning method is employed for selecting equipment for the process according to two different rules, and three known effective scheduling rule is employed for process arrangement; 3, crossing and mutation operations employ a genetic operator based on process priority protection; 4, before mutation, the probability of individual and genetic mutation is calculated through a formula, thereby achieving more accordance with the natural law. The algorithm is high in practicality.

Description

[0001] Technical field [0002] The invention relates to the technical field of job shop scheduling, in particular to the technical field of flexible job shop scheduling with strong practicability. Background technique [0003] Job Shop Scheduling Problem (JSP), as one of the typical combinatorial optimization problems, its research began in the 1950s, and it can be traced back to 1954, when scientists put forward the flow shop scheduling problem of two machine tools and solve. In recent decades, more and more scholars have devoted themselves to the research of JSP due to the needs of actual production and the continuous introduction of related technologies, especially intelligent optimization algorithms. From single-resource constraints to multi-resource constraints, deterministic to uncertain, single-objective to multi-objective, small-scale to large-scale, all kinds of JSPs have been extensively studied. And the research results of some intelligent scheduling methods have...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B19/418
CPCG05B19/41865G05B2219/32252Y02P90/02
Inventor 胡成华
Owner SICHUAN YONGLIAN INFORMATION TECH CO LTD
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