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Job-shop scheduling method based on an improved genetic algorithm

An improved genetic algorithm and workshop scheduling technology, applied in the field of single-piece workshop scheduling, can solve problems such as unsatisfactory evolutionary optimization and high complexity, and achieve the effects of shortening processing time, ensuring diversity, and accelerating convergence speed

Inactive Publication Date: 2018-11-13
SHANGHAI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Single-piece workshop scheduling is an NP-hard problem with a high degree of complexity
When the genetic algorithm solves the single-piece shop scheduling problem, it is prone to premature convergence, which makes the evolutionary optimization unsatisfactory. Therefore, it is necessary to carry out research and improve the genetic algorithm

Method used

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  • Job-shop scheduling method based on an improved genetic algorithm
  • Job-shop scheduling method based on an improved genetic algorithm
  • Job-shop scheduling method based on an improved genetic algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0041] Embodiment one: see figure 1 , the single-piece shop-shop scheduling method based on the improved genetic algorithm, which is characterized by:

[0042] The operation steps are as follows:

[0043] Step 1: Determine the operating parameters.

[0044] Step 2: Improved initial population generation.

[0045] Step 3: Calculation of individual fitness.

[0046] Step 4: Select Actions.

[0047] Step 5: Crossover operation.

[0048] Step 6: Mutation operation.

[0049] Step 7: Terminate discrimination.

Embodiment 2

[0050] Embodiment 2: This embodiment is basically the same as Embodiment 1, and the special features are as follows:

[0051] The operating parameters are the population size M, the crossover probability Pc, the mutation probability Pm and the number of iterations T.

[0052]The improved initial population generation is as follows: using the process code-based method to obtain process codes, generating individuals, and forming an improved initial population together with individuals obtained by using the minimum and earliest completion time method.

[0053] The calculation of the individual fitness is: according to the earliest completion time criterion, select machines for each workpiece process in the individual, and calculate the total completion time, and use the reciprocal of the total completion time as the fitness value; obviously, the shorter the completion time, the greater the fitness. The higher the value.

[0054] The selection operation adopts a roulette selectio...

Embodiment 3

[0058] Embodiment three: see Figure 1~Figure 3 , the operation steps of this single-piece shop-shop scheduling method based on the improved genetic algorithm are detailed as follows:

[0059] Step 1. Determine the operating parameters

[0060] The operating parameters of the genetic algorithm: the population size M is generally 20~100, the crossover probability Pc is generally 0.4~0.99, the mutation probability Pm is generally 0.0001~0.1, and the number of iterations is generally 100~500.

[0061] Step 2. Improved initial population generation

[0062] The generation of the initial population relies on encoding to obtain the process code. The encoding method based on the process is: each workpiece is represented by the corresponding workpiece number, and the order in which the same workpiece number appears represents the process corresponding to the workpiece number. For the sorting problem of n workpieces and m procedures, the individual procedure code has n×m gene bits, a...

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Abstract

The invention discloses a job-shop scheduling method based on an improved genetic algorithm. The method comprises the operation steps of (1) determining operation parameters, including a population scale, a crossover probability, a variation probability and the number of iterations; (2) generating an improved initial population, and combining individuals generated by adopting an active method withindividuals generated randomly in a certain proportion to form an initial population; (3) performing fitness calculation, and taking a reciprocal of the total completion time of a scheduling scheme corresponding to the individuals as a fitness value; (4) performing selection operation by adopting a roulette selection operator; (5) performing crossover operation by selecting and using a POX crossover operator; (6) selecting and using an inverse variation operator as a variation operator; and (7) performing stop judgment, judging whether a stop condition is met or not, and if yes, stopping theprocess and outputting an optimal scheduling scheme, otherwise, going to the step (3). According to the method, the convergence speed of solving can be increased; the solving performance is good; andfor the production scheduling problem of job-shop, the method has high application values.

Description

technical field [0001] The invention relates to a single-piece workshop scheduling method based on an improved genetic algorithm, which uses the genetic algorithm in artificial intelligence algorithms to solve the single-piece workshop scheduling problem. Background technique [0002] In the production operation plan, the variety and quantity of the product are known in the order, and the production time can also be determined by the craftsman, but the arrangement of the production sequence is often difficult to reasonably determine, determine the processing sequence of the workpiece and allocate the corresponding production equipment To process the workpiece, this process is called job sequencing or job scheduling. The purpose of job scheduling is to optimize the production process by determining a reasonable processing sequence for the workpiece on the machine, thereby shortening the production cycle and improving equipment utilization. [0003] The methods of job shop sc...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06N3/00
CPCG06N3/006G06Q10/06316
Inventor 黄宗南周帅
Owner SHANGHAI UNIV
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