Title : 
Research on partner selection problem of virtual enterprise based on improved genetic algorithm
         
        
            Author : 
Zhou Wei ; Bu Yan-ping ; Zhou Ye-qing
         
        
            Author_Institution : 
Sch. of Bus., East China Univ. of Sci. & Technol., Shanghai, China
         
        
        
            fDate : 
May 31 2014-June 2 2014
         
        
        
        
            Abstract : 
The partner selection and optimization problem is an important area of virtual enterprise (VE). Genetic algorithm (GA) is optimization and parallel strategy simulating biology evolutionary mechanism in nature and a high efficient algorithm solving these types of problems. After analyzing the partner selection problems of virtual enterprise, the improved genetic algorithm (IGA) was presented to solve enterprise alliance problem within reasonable time and cost. There are certain number partners of each sub-task in virtual enterprise environment. The objective is, by selecting the optimal combination of partners, to minimize project´s completion time and project´s total cost. A set of experiments show that the algorithm is stable and presents low variability. We analyze the laboratory results to show that the improved algorithm has better characteristics than standard GA when it was used in partner selection problems.
         
        
            Keywords : 
genetic algorithms; virtual enterprises; IGA; VE; biology evolutionary mechanism; enterprise alliance problem; improved genetic algorithm; parallel strategy; partner selection problem; virtual enterprise environment; Biological cells; Educational institutions; Genetic algorithms; Simulated annealing; Sociology; Virtual enterprises; genetic algorithm; partner selection; simulated annealing; virtual enterprise;
         
        
        
        
            Conference_Titel : 
Control and Decision Conference (2014 CCDC), The 26th Chinese
         
        
            Conference_Location : 
Changsha
         
        
            Print_ISBN : 
978-1-4799-3707-3
         
        
        
            DOI : 
10.1109/CCDC.2014.6852319