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Flexible job shop batch scheduling method based on genetic algorithm

A technology of flexible operation and genetic algorithm, applied in the direction of genetic rules, calculation, genetic model, etc., can solve the problems of increased complexity, high complexity, and complex problems of evolutionary operations

Inactive Publication Date: 2017-07-21
SHANGHAI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Flexible workshop scheduling is an NP-hard problem, which has a high degree of complexity in workshop scheduling, and adding batching problems on the basis of it makes the problem more complicated
When the genetic algorithm solves the batch scheduling problem of the flexible workshop, the complexity of the evolutionary operation is greatly increased due to the difference in batches, which makes the evolutionary optimization unsatisfactory. Therefore, it is necessary to carry out research and formulate a suitable method

Method used

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  • Flexible job shop batch scheduling method based on genetic algorithm
  • Flexible job shop batch scheduling method based on genetic algorithm
  • Flexible job shop batch scheduling method based on genetic algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0082] Embodiment one: as attached figure 1 As shown, the operation steps of this flexible workshop batch scheduling method based on genetic algorithm are as follows:

[0083] Step 1, determine the operating parameters.

[0084] Step 2, initial population generation.

[0085] Step 3, individual fitness calculation.

[0086] Step 4, select an operation.

[0087] Step five, cross operation.

[0088] Step six, mutation operation.

[0089] Step seven, terminate the judgment.

Embodiment 2

[0090] Embodiment two: see Figure 1 to Figure 5 , the present embodiment is basically the same as Embodiment 1, and the special features are as follows:

[0091] The operating parameters are population size M, crossover probability P C , the mutation probability P M and the number of iterations T.

[0092] When the initial population is generated, a segmented coding method is used to obtain batch codes and process codes to generate individuals, and the batch codes are generated using a roulette method.

[0093] When calculating the individual fitness, select machines for each batch of workpieces according to the earliest completion criterion of the workpiece, calculate the time, obtain the total completion time, and then take the reciprocal of the total completion time as the fitness value; obviously, the shorter the completion time, the higher the fitness value. high.

[0094] The selection operation adopts a roulette selection method, and the selection probability depen...

Embodiment 3

[0098] Embodiment 3: This embodiment is basically the same as Embodiment 1, and the special features are as follows:

[0099] The specific details of each step are as follows:

[0100] Step 1. Determine the operating parameters

[0101] The operating parameters of the genetic algorithm include population size M, crossover probability P C , the mutation probability P M , the number of iterations T. The population size M is generally 20-100, and the crossover probability P C Generally take 0.4 ~ 0.99, the variation probability P M Generally, it is 0.0001~0.1, and the number of iterations T is generally 100~500.

[0102] Step 2. Initial Population Generation

[0103] The generation of the initial population depends on coding. The coding of the flexible job shop batch scheduling problem needs to contain two parts of information. The first part is the batch information of each workpiece, which determines how many batches each workpiece is divided into. The number of workpiece...

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Abstract

The invention discloses a batch scheduling method for flexible job shops based on genetic algorithms. The steps of the method are: (1) Determine the operating parameters, including population size M, crossover probability P C , the mutation probability P M , the number of iterations T; (2) Initial population generation, using segmented coding method to generate batch codes and process codes; (3) Individual fitness calculation, taking the reciprocal of the individual’s total completion time as its fitness value; (4) Select Operation, using the roulette selection operator; (5) crossover operation, setting crossover execution criteria, performing crossover on batch codes or process codes according to the criteria, and repairing after crossover; (6) mutation operation, using multiple Point mutation, using reverse sequence mutation for the process code; (7) Termination discrimination, judging whether the number of generations meets the termination condition, stop if it is satisfied, and output the optimal scheduling plan, otherwise go to (3). The invention can optimize the production operation of the flexible workshop, effectively shorten the production cycle, has strong applicability and is easy to popularize.

Description

technical field [0001] The invention relates to a production plan formulation in intelligent manufacturing technology, specifically a genetic algorithm-based flexible job shop batch scheduling method, which uses the genetic algorithm in the artificial intelligence algorithm to solve the flexible job shop batch scheduling problem. Background technique [0002] In the production operation plan, the production variety and production 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 determine reasonably. To determine the processing sequence of the workpiece and assign the corresponding Production equipment to process workpieces, this process is called job sequencing or job scheduling. For the same batch of workpieces to be processed, different processing sequences will result in different processing completion times, which greatly affects the produc...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/04G06N3/12
CPCY02P90/30G06Q10/04G06N3/126G06Q10/06311G06Q50/04
Inventor 黄宗南张海水周帅贾亚飞
Owner SHANGHAI UNIV
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