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Cloud manufacturing service combination optimization selection method based on preference NSGA-III algorithm

A service combination and optimization selection technology, applied in manufacturing computing systems, calculations, genetic models, etc., to achieve the effects of improving solution efficiency, satisfaction, and solution capabilities

Pending Publication Date: 2020-06-02
SHENYANG GOLDING NC & INTELLIGENCE TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Aiming at the deficiencies of the prior art, the present invention provides a cloud manufacturing service combination optimization selection method based on the preference NSGA-Ⅲ algorithm, which solves the trade-off optimization problem of QoS indicators in the service combination optimization selection in the cloud manufacturing environment, thereby improving the solution efficiency and solving Ability to help service requesters find a set of combined services that meet the requester's preference requirements, which has engineering guiding significance for scheduling optimization of cloud manufacturing

Method used

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  • Cloud manufacturing service combination optimization selection method based on preference NSGA-III algorithm
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  • Cloud manufacturing service combination optimization selection method based on preference NSGA-III algorithm

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Embodiment

[0064] According to user needs, the embodiment selects QoS index values, which are processing time, manufacturing cost, product quality, delay risk, and service satisfaction; among them,

[0065] The processing time is composed of two parts: the processing time for the manufacturing resource to complete the unit task and the transportation time when the task is transferred between different manufacturing resources;

[0066] Manufacturing costs are composed of two parts, direct production and processing costs and transportation costs;

[0067] Product quality, which is the pass rate for completing processing tasks;

[0068] Delay risk is the failure rate of manufacturing resources when processing task units;

[0069] Service satisfaction is the satisfaction evaluation of the service requester for the service provided.

[0070] Step 4: Using non-dominated sorting method, R t Divided into different non-dominated layers (F 1 ,F 2 ,...) and retain the high priority non-dominated set to the ne...

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Abstract

The invention relates to a cloud manufacturing service combination optimization selection method based on a preference NSGA-III algorithm, and the method comprises the steps: generating an initial population according to a coding operation method, and generating filial generation individuals; decoding all individuals in the current population, and calculating a combined service QoS index value according to the decoded individuals; dividing Rt into different non-dominated layers by adopting a non-dominated sorting method, defining F1 as a critical layer, selecting K excellent individuals from F1 by adopting a critical layer individual selection method and retaining the K excellent individuals in Pt + 1 at the same time, and in Pt + 1, using a modality algorithm to select elite individuals for further local search. According to the method, the decision preference information is introduced into the algorithm in an interactive mode by adaptively constructing the preference reference points, so that the solving efficiency of the algorithm is improved; meanwhile, aiming at the problem that the local search capability of the algorithm is insufficient, the evolutionary mechanism of the algorithm is improved, and a genetic search and local search mixed modality algorithm is provided to improve the solving capability of the algorithm.

Description

Technical field [0001] The invention relates to the field of cloud manufacturing service scheduling, in particular to a cloud manufacturing service combination optimization selection method based on a preference NSGA-Ⅲ algorithm. Background technique [0002] Cloud manufacturing is a new intelligent manufacturing model based on cloud computing, Internet of Things, big data, and service-oriented technology. It integrates social manufacturing resources through networks and cloud platforms, thereby effectively improving resource utilization and reducing production costs. Provide users with personalized services. In order to realize the centralized management of various manufacturing resources in a heterogeneous environment, service providers virtualize and serve various manufacturing resources and manufacturing capabilities as manufacturing cloud services and publish them to the cloud manufacturing service platform. The cloud manufacturing service platform is responsible for manufac...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/04G06N3/12
CPCG06Q10/04G06Q50/04G06N3/126Y02P90/30
Inventor 胡毅于东毕筱雪刘劲松韩旭于皓宇
Owner SHENYANG GOLDING NC & INTELLIGENCE TECH CO LTD
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