Please use this identifier to cite or link to this item: https://doi.org/10.1080/00207543.2011.561883
Title: GA-BHTR: An improved genetic algorithm for partner selection in virtual manufacturing
Authors: Tao, F.
Qiao, K.
Zhang, L.
Li, Z.
Nee, A.Y.C. 
Keywords: binary heap
catastrophe
genetic algorithm (GA)
mutiple communities
partner selection problem (PSP)
transitive reduction
virtual manufacturing
Issue Date: 15-Apr-2012
Source: Tao, F., Qiao, K., Zhang, L., Li, Z., Nee, A.Y.C. (2012-04-15). GA-BHTR: An improved genetic algorithm for partner selection in virtual manufacturing. International Journal of Production Research 50 (8) : 2079-2100. ScholarBank@NUS Repository. https://doi.org/10.1080/00207543.2011.561883
Abstract: An evolutionary genetic algorithm maintained using the binary heap and transitive reduction (GA-BHTR) method for addressing the partner selection problem (PSP) in a virtual enterprise is proposed. In order to reduce the time complexity of PSP, an algorithm for simplifying the directed acyclic graph that represents the precedence relationship among the subprojects in PSP is first designed. Different from the traditional regular GA, in order to avoid solutions from converging to a constant value early during evolution, multiple communities are used instead of a single community in GA-BHTR. The method and algorithms to distribute the individuals to the multiple communities while maximising the differences among the different communities are proposed. The concept of the catastrophe is introduced in the proposed GA-BHTR in order to avoid the solutions from converging to a local best solution too early after several generations of evolution. In order to maintain the capacity of the community (i.e. the number of individuals existing in a community) at a constant value while enhancing the diversity of the proposed GA-BHTR, an algorithm using the binary heap to maintain the data is designed. Simulation and experiments are conducted to test the effectiveness and performance of the proposed GA-BHTR for addressing PSP. © 2012 Copyright Taylor and Francis Group, LLC.
Source Title: International Journal of Production Research
URI: http://scholarbank.nus.edu.sg/handle/10635/60392
ISSN: 00207543
DOI: 10.1080/00207543.2011.561883
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

26
checked on Dec 11, 2017

WEB OF SCIENCETM
Citations

21
checked on Dec 11, 2017

Page view(s)

69
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.