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Research on Job-shop Scheduling Problem Based on Immune Genetic Algorithm

Author: YangDaoWen
Tutor: LiuQuan
School: Suzhou University
Course: Applied Computer Technology
Keywords: Genetic Algorithms Immune Genetic Algorithm Multi-objective optimization Job Shop Scheduling
CLC: TP18
Type: Master's thesis
Year: 2009
Downloads: 72
Quote: 2
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Abstract


Shop scheduling problem is the focus of widespread concern by many experts and scholars , the genetic algorithm optimization algorithm to solve the problem , genetic algorithm optimization efficiency on the basis of the study , proposed an improved immune genetic algorithm , this algorithm will be applied to the metal processing plant in solving scheduling problems . This study mainly includes the following four aspects : ( 1 ) described for the shop scheduling problem goals shop scheduling problems to study the genetic algorithm is applied to job shop scheduling problems . The experiments show that the genetic algorithm is applied in the shop scheduling problems are more advantages than other algorithms . (2 ) for the basic immune algorithm to solve multi-objective optimization problem , based on artificial immune algorithm based on information entropy niche technology , an improved immune genetic algorithm . Multimodal function optimization show that the improved algorithm has better convergence speed and global search ability . (3) by a mathematical model of a metal processing plant analysis , design optimization function , the improved algorithm is applied to the design of the actual shop scheduling parameters . The experiments show that the algorithm is feasible and efficient . ( 4 ) based on the improved immune genetic algorithm , we designed and implemented a the metalworking shop scheduling system simulation platform . Experimental results show that the improved algorithm is better than traditional algorithm scheduling solution .

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory
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