Fuzzy job-shop scheduling method based on self-adaption inheritance and clonal selection algorithm

A clone selection algorithm and job shop technology, applied in the field of processing workpieces and machine deployment, can solve problems such as random update populations, and achieve the effect of improving efficiency and shortening fuzzy completion time.

Inactive Publication Date: 2015-01-14
DONGHUA UNIV
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Problems solved by technology

[0007] (3) The update popu

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  • Fuzzy job-shop scheduling method based on self-adaption inheritance and clonal selection algorithm
  • Fuzzy job-shop scheduling method based on self-adaption inheritance and clonal selection algorithm
  • Fuzzy job-shop scheduling method based on self-adaption inheritance and clonal selection algorithm

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Embodiment Construction

[0029] In order to make the present invention more comprehensible, preferred embodiments are described in detail below with accompanying drawings. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0030] The invention provides a fuzzy job shop scheduling method based on adaptive genetic and clone selection algorithm, the steps of which are:

[0031] Step 1. Determine the coding scheme for the fuzzy shop-shop scheduling problem, specifically:

[0032] The coding scheme of fuzzy shop scheduling is determined to be the coding method based on the process, each gene represen...

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Abstract

The invention relates to a fuzzy job-shop scheduling method based on self-adaption inheritance and the clonal selection algorithm. The method includes the steps of determining the coding scheme of the fuzzy job-shop scheduling problem, generating the initial population N at random, defining a solution space, defining and calculating the adaptability function of an individual, conducting clone proliferation operation on the individual according to the size of the adaptability value near each generation of optimal solution, independently conducting self-adaption cross and mutation operation on individuals obtained through reproduction, conducting clonal selection operation on the individuals obtained through cross and mutation to generate the new population N*, ending the circulation if the ending condition is met, and returning to continue the next step if the ending condition is not met. By means of the method, resources can be more reasonably distributed, the completion time of job-shop scheduling fuzziness is shortened, the efficiency of job-shop fuzzy scheduling is improved, and the requirement of actual production scheduling is better met.

Description

technical field [0001] The invention relates to an allocation method of processing workpieces and machines in operation scheduling of industrial workshops. Background technique [0002] Since the production planning and control problems of enterprise workshops are the most typical of all scheduling problems, the research on workshop scheduling problems has always occupied a dominant position in scheduling theory. The uncertain factors faced in the production process, such as: machine failure, machine processing delay, uncertainty of workpiece processing time, ambiguity of delivery date of each workpiece, etc., will affect the entire scheduling arrangement and the evaluation of the scheduling results. The research on fuzzy shop-shop scheduling problem is more in line with actual production, so more and more scholars begin to study fuzzy shop-shop scheduling problem. [0003] So far, Han S et al. have studied the single-machine scheduling problem with fuzzy delivery date; SAK...

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Application Information

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IPC IPC(8): G06Q10/06G06N3/12
CPCG06Q10/0631G06N3/126
Inventor 高尚策陈贝贝沈冬梅侍倩柴宏建吴再新
Owner DONGHUA UNIV
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