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Multi-constraint-condition parallel task scheduling method

A task scheduling and multi-constraint technology, applied in data processing applications, instruments, computing, etc., can solve problems such as long optimization time, large amount of calculation, and unsuitable optimization technology

Active Publication Date: 2020-07-07
HOHAI UNIV CHANGZHOU
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0003] Since the production scheduling problem is a type of NP-Hard, it is not suitable for traditional optimization techniques to solve
These production scheduling methods centered on intelligent optimization algorithms have a large amount of calculation and a long time to optimize, often appearing the phenomenon of "premature convergence"

Method used

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  • Multi-constraint-condition parallel task scheduling method
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Embodiment Construction

[0026] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0027] Assuming that there is a scheduling task consisting of four parallel tasks, the preset completion time (unit: minute) and number of participants of each subtask are shown in Table 1. The total number of people performing the task is 5, and the subtasks in the same task are executed according to the sequence number, and it is required that subtask 3 of task A must be completed before subtask 2 of task B; the candidate solution scale is 27, divided into three There are 9 candidate solutions in each group; the duration of the shortest completion time is 5 times.

[0028] Table 1 Schedule of preset time and number of participants for scheduling tasks

[0029]

[0030]

[0031] Step 1,...

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Abstract

The invention discloses a multi-constraint-condition parallel task scheduling method, which comprises the following steps of: 1, an initialization process: setting a feature number for each parallel task, constructing a number pool of a candidate solution coding string, and setting an initial feasible solution range of a candidate solution; 2, a repeating optimization process: generating a plurality of candidate solution coding strings by utilizing the number pool and the feasible solution range; calculating the fitness value of each code string, and taking the candidate solution code string with the optimal fitness value as an optimal scheduling scheme if an iteration termination condition is met; otherwise, performing grouping statistics on the distribution model of the candidate solution coding strings, and calculating a feasible solution range of each group of coding strings; and generating a new candidate solution coding string from the feasible solution range, and then repeatingthe optimization process. According to the method, grouping optimization and search range self-adaptive adjustment strategies are adopted, so that the optimal scheduling scheme can be searched in a very short time.

Description

technical field [0001] The invention relates to a multi-constraint parallel task scheduling method, which belongs to the technical field of production scheduling. Background technique [0002] Production scheduling is the work of arranging process execution process and personnel allocation according to certain production operation conditions. A reasonable scheduling plan can efficiently allocate production labor and improve work efficiency. [0003] Since the production scheduling problem is a type of NP-Hard, it is not suitable for solving by traditional optimization techniques. In order to effectively solve the production scheduling problem, the patents with application numbers CN201610281979.3, CN201710866667.3 and CN201710965924.9 respectively use evolutionary algorithm, improved empire competition algorithm and firefly algorithm to realize task scheduling in flexible workshops; application number is CN201710013045.6 and CN201610628188.3 patents use genetic algorithms ...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/04
CPCG06Q10/06G06Q50/04Y02P90/30
Inventor 杨启文余诗琦薛云灿陈俊风
Owner HOHAI UNIV CHANGZHOU