Title :
Energy adaptive immune genetic algorithm for collaborative design task scheduling in Cloud Manufacturing system
Author :
Laili, Yuanjun ; Zhang, Lin ; Tao, Fei
Author_Institution :
Sch. of Autom. Scicence & Electr. Eng., Beihang Univ., Beijing, China
Abstract :
A new manufacturing paradigm, i.e. Cloud Manufacturing (CMfg) has been proposed recently. In order to satisfy high efficiency and low cost collaborative design task scheduling in CMfg, a new energy adaptive immune genetic algorithm (EAIGA) was designed. With the introduction of potential energy storage and detection, the new algorithm can not only improve searching diversity based on immune strategy, but also adaptively adjust the probabilities of crossover and mutation with low time complexity. The experimental results demonstrate that the new algorithm can effectively solving collaborative design task scheduling problem with a good balance between searching diversification and intensification.
Keywords :
computational complexity; design for manufacture; genetic algorithms; manufacturing systems; probability; scheduling; cloud manufacturing system; collaborative design task scheduling; crossover probability; energy adaptive immune genetic algorithm; energy detection; energy storage; mutation probability; time complexity; Algorithm design and analysis; Collaboration; Genetic algorithms; Immune system; Job shop scheduling; Potential energy; Collaborative design task scheduling; adaptive immune genetic algorithm;
Conference_Titel :
Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4577-0740-7
Electronic_ISBN :
2157-3611
DOI :
10.1109/IEEM.2011.6118248