Self-adapted hybrid genetic algorithm for job scheduling of distributed manufacturing system

2007 ◽  
Author(s):  
Jingsong Yang ◽  
Guangcai Cui ◽  
Xuedan Hu
Keyword(s):  
Genetic Algorithm ◽  
Manufacturing System ◽  
Job Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Distributed Manufacturing

Integrated Scheduling of the Job and AGV for Flexible Manufacturing System

2011 ◽  
Vol 80-81 ◽  
pp. 1335-1339 ◽  
Author(s):  
You Long Lv ◽  
Gong Zhang ◽  
Jie Zhang ◽  
Yi Jun Dong
Keyword(s):  
Genetic Algorithm ◽  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Job Scheduling ◽  
Mathematic Model ◽  
Integrated Scheduling ◽  
Scheduling Model ◽  
Problem Description ◽  
Equipment Utilization ◽  
Moving Path

Job scheduling and AGV scheduling in FMS are regarded as two independent problems by most researchers. Their isolation ignores AGV’s use conflicts in the job scheduling and leads to low average equipment utilization. We point out the necessity for the job scheduling to integrate with AGV scheduling through analyzing scheduling problem of a specific type of FMS with single AGV and single buffer area. Then a corresponding mathematic model for integrated scheduling is presented based on the problem description and constraints for scheduling. A specific FMS is adopted to validate this integrated scheduling model. Based on data from this FMS, the model is performed through genetic algorithm with appropriate parameters. And job’s processing sequences as well as AGV’s moving path are obtained from the optimal gene order. The experiment result of this scheduling model adopting genetic algorithm shows good computing efficiency and equipment utilization.

Design of GA and Ontology based NLP Frameworks for Online Opinion Mining

2019 ◽  
Vol 13 (2) ◽  
pp. 159-165
Author(s):  
Manik Sharma ◽  
Gurvinder Singh ◽  
Rajinder Singh
Keyword(s):  
Genetic Algorithm ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Opinion Mining ◽  
Hybrid Genetic Algorithm ◽  
Online Reviews ◽  
Middle Tier ◽  
Complete Set ◽  
Mining Model

Background: For almost every domain, a tremendous degree of data is accessible in an online and offline mode. Billions of users are daily posting their views or opinions by using different online applications like WhatsApp, Facebook, Twitter, Blogs, Instagram etc. Objective: These reviews are constructive for the progress of the venture, civilization, state and even nation. However, this momentous amount of information is useful only if it is collectively and effectively mined. Methodology: Opinion mining is used to extract the thoughts, expression, emotions, critics, appraisal from the data posted by different persons. It is one of the prevailing research techniques that coalesce and employ the features from natural language processing. Here, an amalgamated approach has been employed to mine online reviews. Results: To improve the results of genetic algorithm based opining mining patent, here, a hybrid genetic algorithm and ontology based 3-tier natural language processing framework named GAO_NLP_OM has been designed. First tier is used for preprocessing and corrosion of the sentences. Middle tier is composed of genetic algorithm based searching module, ontology for English sentences, base words for the review, complete set of English words with item and their features. Genetic algorithm is used to expedite the polarity mining process. The last tier is liable for semantic, discourse and feature summarization. Furthermore, the use of ontology assists in progressing more accurate opinion mining model. Conclusion: GAO_NLP_OM is supposed to improve the performance of genetic algorithm based opinion mining patent. The amalgamation of genetic algorithm, ontology and natural language processing seems to produce fast and more precise results. The proposed framework is able to mine simple as well as compound sentences. However, affirmative preceded interrogative, hidden feature and mixed language sentences still be a challenge for the proposed framework.

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