Design and Application of A New Hybrid Heuristic Algorithm for Flow Shop Scheduling
Author(s) -
Fang Wang,
Yunqing Rao,
Yu Hou
Publication year - 2011
Publication title -
international journal of computer network and information security
Language(s) - English
Resource type - Journals
eISSN - 2074-9104
pISSN - 2074-9090
DOI - 10.5815/ijcnis.2011.02.06
Subject(s) - computer science , heuristic , population , benchmark (surveying) , genetic algorithm , algorithm , null move heuristic , mathematical optimization , matlab , job shop scheduling , scheduling (production processes) , flow shop scheduling , population based incremental learning , mathematics , artificial intelligence , machine learning , schedule , demography , geodesy , sociology , geography , operating system
a new heuristic algorithm was designed by combining with Johnson method, NEH method and characteristics of scheduling, and it was implemented on MATLAB. The efficiency of the new algorithm was tested through eight Car questions and two Hel questions of Benchmark problems, and the results revealed that the new heuristic algorithm was better than the other three heuristic algorithms. Further more; the application of this heuristic algorithm in the intelligent algorithm especially in the genetic algorithms (GA) was discussed. Two GAs were designed for Flow Shop question, and they had the same processes and the same parameters. The only difference is in the production of the initial population. One GA's initial population is optimized by the new heuristic algorithm, and the other whose initial population is randomly generated entirely. Finally, through the test of eight Car questions, it is demonstrated that the heuristic algorithm can indeed improve efficiency and quality of genetic algorithm because the heuristic algorithm can improve the initial population of GA.
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