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Order insertion scheduling method based on genetic algorithm and fireworks algorithm

A technology of fireworks algorithm and genetic algorithm, which is applied in the field of order insertion and scheduling based on genetic algorithm and fireworks algorithm, can solve the problems of large time loss, low scheduling efficiency, and increased frequency of order insertion, so as to improve accuracy and improve Efficiency and the effect of reducing time loss

Pending Publication Date: 2021-10-15
宁波沙塔信息技术有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Due to the gradual popularity of multi-variety and small-batch production methods, production scheduling scenarios have become more complex, and the frequency of order insertion has also greatly increased
[0004] The existing technology usually adopts the method of sequential order insertion when solving the order insertion problem. This method has a series of problems such as low scheduling efficiency, large time loss, and inaccurate solution when dealing with the order insertion problem in the multi-variety and small-batch production mode.

Method used

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  • Order insertion scheduling method based on genetic algorithm and fireworks algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0074] Example 1: As shown, a plug-in scheduled method based on genetic algorithm and firework algorithm, including the following steps:

[0075] S1 sets the initial parameters, the initial parameters include population scale, elite scale h, number of variations, and total iterative;

[0076] S2 Select the interlocking mode for order sorting scheduled scheme or the smallest interlocking mode based on the loss;

[0077] S3 gets original orders and plug orders, generates initial seeds according to the set population, and starts iteration as the current population;

[0078] S4 performs population crossings for the current population to obtain a crossworm;

[0079] S5 performs a group variant of a crossed population according to the number of set variations, and is obtained by genetic groups;

[0080] S6 calculates the size of the fireworks based on the dynamic regulating mechanism based on the convergence, and remembers F, Among them, C represents the convergence index, and a repres...

Embodiment 2

[0087] Embodiment 2: Selecting the interpretation schedule in step S2 is based on order sorting mode;

[0088] The specific method of generating the initial population in step S3 is:

[0089] A-1 Get the order rights sequence, determine that the plug order is inserted into the plug order in the order rights sequence, and obtains the order sequence after the plug order is in the plug position in the order rights sequence.

[0090] A-2 acquires the original gene sequence fragment corresponding to each original order in the order sequence, and performs Gaussian variation from each original gene sequence sequence to the Gaussian variation, which is used for each plug order in the order sequence. The randomly generated manner generates its corresponding gene sequence fragment, and the gene sequence fragment corresponding to the Gaussian variation of the Gaussian desert sequence is serially connected in series in order of the order sequence, and the total gene sequence is used as one Th...

Embodiment 3

[0112] Embodiment 3: Selecting the interpretation scheme in step S2 is based on the minimum loss of loss;

[0113] The specific method of generating the initial population in step S3 is:

[0114] I-1 After encoding the procedure in the plug order, i-1 randomly generates a gene fragment, and records G1, obtains the original gene sequence corresponding to the original order, and performs Gaussian variation of Gaussi variation after each original gene sequence. And recorded as G2;

[0115] I-2 Compositions of gene sequences of initial individuals in accordance with G1 in front of G2;

[0116] I-3 Repeat steps I-1 to I-2 until the number of initial individuals reaches the set population size, and all of the resulting initial individuals constitute initial groups;

[0117] In step S4, the current population is cross-operated, and the specific method of obtaining the cross-over population is:

[0118] J-1 is generated in the [1, N) interval, generates a random number K "', where n repre...

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Abstract

The invention discloses an order insertion scheduling method based on a genetic algorithm and a fireworks algorithm. The order insertion scheduling method is characterized by comprising the steps of setting initial parameters; selecting an order insertion scheduling mode; obtaining an original order and an order insertion order, generating an initial population, and starting iteration by taking the initial population as a current population; performing population crossover and mutation operation on the current population; calculating the firework scale to obtain a firework group; performing firework explosion and Gaussian mutation operation on the firework group; selecting fireworks; carrying out genetic selection; judging whether a set total number of iterations is reached or not after one-time iteration, and if yes, outputting a hybrid firework selection and genetic selection population; otherwise, continuing iteration until the set total number of iterations is reached; the method has the advantages that the production orders with the inserted orders are scheduled through the method, the production scheduling efficiency is improved, the time loss is reduced, and the precision of the solving result is improved.

Description

Technical field [0001] The present invention relates to the field of interlocking scheduling technology, in particular, a plug-in scheduled method based on genetic algorithms and fireworks algorithms. Background technique [0002] As customers are more diversified, the market competition is more fierce, multi-variety small batch production methods are gradually popular; multi-variety small batch refers to many kinds of goods that need to be produced during a certain period of time, and each product is required to produce There is less quantity. [0003] Since the multi-variety of small batch production methods are gradually popular, the production scheduled scene has also become more complex, in which the frequency of the plug is greatly increased. [0004] Prior art typically uses the delayed plug while solving the interlocking problem, this method is a series of problems such as low-planning efficiency, time loss, not accurate, and inaccurate induction of plug-in problems in a ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06N3/12G06Q50/04
CPCG06Q10/0631G06N3/126G06Q50/04Y02P90/30
Inventor 吴连秋马开凯钟钊瑜
Owner 宁波沙塔信息技术有限公司
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