Fast non-dominated sorting genetic algorithm for dynamic equivalent parallel machine scheduling problem

A non-dominated sorting, efficient parallel computer technology, applied in the fields of genetic laws, computing, office automation, etc., to achieve the effect of achieving diversity and convergence, improving search efficiency and solution quality

Pending Publication Date: 2022-05-31
WENZHOU UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The present invention can solve the problem of rapid generation of multi-objective scheduling decisions under the environment of elastic preventive maintenance of machines and uncertain arrival time of workpieces, and at the same time improve the completion time of workpieces and reduce the delay rate of orders

Method used

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  • Fast non-dominated sorting genetic algorithm for dynamic equivalent parallel machine scheduling problem
  • Fast non-dominated sorting genetic algorithm for dynamic equivalent parallel machine scheduling problem
  • Fast non-dominated sorting genetic algorithm for dynamic equivalent parallel machine scheduling problem

Examples

Experimental program
Comparison scheme
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Embodiment

[0099] Embodiment: a kind of improved fast non-dominated sorting genetic algorithm facing dynamic equivalent parallel machine scheduling problem, such as figure 1 shown, follow the steps below:

[0100] s1: Generate an initial population P of size N by mixing heuristic rules MODD (Modified due date), ATC (Apparent tardiness cost), X-RM (X-dispatch ATC) and random rules t .

[0101] S2: Using the decoding method based on dynamic programming, calculate the maximum completion time C of each individual max and the total overdue time and ∑T j (Total Tardiness).

[0102] s3: Determine whether the termination condition is met. If yes, end; otherwise, for population P t The individuals in are subjected to quick non-dominated sorting.

[0103] s4: Generate offspring population Q through genetic operators such as adaptive probability crossover and mutation, elite selection strategy t .

[0104] S5: the parent population P t and offspring population Q t A population R of size ...

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Abstract

The invention discloses a fast non-dominated sorting genetic algorithm for a dynamic equivalent parallel machine scheduling problem. The algorithm comprises the following steps: S1, generating a population by using a hybrid heuristic rule MODD, ATC, X-RM and a random method; s2, target value calculation is carried out on population individuals based on a dynamic programming decoding method; s3, judging whether a termination condition is met or not; if yes, ending; otherwise, performing rapid non-dominated sorting on the individuals in the population; s4, generating a filial generation population through a genetic operator; s5, combining the parent population and the child population into a population with the scale of 2N; s6, performing rapid non-dominated sorting on individuals in the population, and determining the population in combination with crowding degree calculation; and S7, performing neighborhood search on population individuals to generate a new solution to replace an original redundant solution. The method has the characteristics of improving the workshop machine utilization rate and meeting the customer delivery time.

Description

technical field [0001] The invention relates to the technical field of computer integrated manufacturing, in particular to a fast non-dominated sorting genetic algorithm oriented to a dynamic equivalent parallel machine scheduling problem. Background technique [0002] With the continuous advancement of the "machine replacement" plan, the traditional "discrete" manufacturing environment has been transformed into a distributed parallel machine operating environment, such as the photolithography process of wafer production, the complex curved surface processing process of aerospace manufacturing, and the converter of steel production In the steelmaking process, the printing and dyeing process of textile production, and the task allocation under the cloud computing environment, parallel machine production has become the most common production form for enterprises. [0003] Parallel Machine Scheduling Problem (Parallel Machine Scheduling Problem, PMSP) is to allocate n workpiece...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q10/10G06N3/12
CPCG06Q10/06311G06Q10/06315G06Q10/06316G06Q10/0633G06Q10/103G06N3/126Y02P90/30
Inventor 陈亚绒王陈黄沈权沈纯纯朱立夏周富得
Owner WENZHOU UNIVERSITY
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