DocumentCode :
2562429
Title :
Fuzzy optimization of knowledge worker scheduling based-on hybrid genetic algorithms
Author :
Wang Qing ; Sun Yong-hang ; Zhao Hui ; Li Xiang
Author_Institution :
Sch. of Manage., Tianjin Univ. of Commerce, Tianjin
fYear :
2008
fDate :
2-4 July 2008
Firstpage :
2615
Lastpage :
2620
Abstract :
Knowledge worker scheduling problem in knowledge enterprise has been known as a NP-hard problem. The knowledge worker scheduling problem allows one task to be implemented on by several knowledge workers of a team. The problem is to assign each task to a knowledge worker and find a sequence for the tasks on the knowledge worker in order that the satisfaction of all customers is maximized. This paper investigated the fuzzy knowledge worker scheduling in knowledge enterprise using hybrid genetic algorithms (HGA) that is based on tabu search, proposed a novel two-chromosome-encoding method, and a case study with the MATLAB 7.0 platform is carried to test this method.
Keywords :
customer satisfaction; genetic algorithms; job shop scheduling; optimisation; personnel; search problems; MATLAB 7.0 platform; NP-hard problem; Tabu search; customer satisfaction; fuzzy optimization; hybrid genetic algorithms; knowledge enterprise; knowledge worker scheduling; two-chromosome-encoding method; Genetic algorithms; Fuzzy Scheduling; Hybrid Genetic Algorithms; Knowledge worker; Tabu Search;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference, 2008. CCDC 2008. Chinese
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-1733-9
Electronic_ISBN :
978-1-4244-1734-6
Type :
conf
DOI :
10.1109/CCDC.2008.4597799
Filename :
4597799
Link To Document :
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