TY - JOUR
T1 - Network optimization in supply chain
T2 - A KBGA approach
AU - Prakash, A.
AU - Chan, Felix T.S.
AU - Liao, H.
AU - Deshmukh, S. G.
N1 - Funding Information:
Dr. Haitao Liao is an Assistant Professor in Department of Industrial & Information Engineering and Nuclear Engineering Department at the University of Tennessee, Knoxville. He received his Ph.D. degree from the Department of Industrial and Systems Engineering at Rutgers University. He also received M.S. degrees in Industrial Engineering and Statistics, both from Rutgers University. His research interests focus on Modeling of Accelerated Testing, Probabilistic Risk Assessment, Maintenance Models and Optimization, Spare Part Inventory Control, and Prognostics. His current research is sponsored by National Science Foundation and U.S. Nuclear Regulatory Commission. He is a member of IIE and INFORMS. He is a recipient of National Science Foundation CAREER Award in 2010 and the 2010 William A.J. Golomski Award.
Funding Information:
The work described in this paper was substantially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. PolyU 510410 ). The authors also thank the editor and the reviewers for their valuable comments and suggestions that have led to the substantial improvement of the paper.
PY - 2012/1
Y1 - 2012/1
N2 - In this paper, we present a Knowledge Based Genetic Algorithm (KBGA) for the network optimization of Supply Chain (SC). The proposed algorithm integrates the knowledge base for generating the initial population, selecting the individuals for reproduction and reproducing new individuals. From the literature, it has been seen that simple genetic-algorithm-based heuristics for this problem lead to and large number of generations. This paper extends the simple genetic algorithm (SGA) and proposes a new methodology to handle a complex variety of variables in a typical SC problem. To achieve this aim, three new genetic operators-knowledge based: initialization, selection, crossover, and mutation are introduced. The methodology developed here helps to improve the performance of classical GA by obtaining the results in fewer generations. To show the efficacy of the algorithm, KBGA also tested on the numerical example which is taken from the literature. It has also been tested on more complex problems.
AB - In this paper, we present a Knowledge Based Genetic Algorithm (KBGA) for the network optimization of Supply Chain (SC). The proposed algorithm integrates the knowledge base for generating the initial population, selecting the individuals for reproduction and reproducing new individuals. From the literature, it has been seen that simple genetic-algorithm-based heuristics for this problem lead to and large number of generations. This paper extends the simple genetic algorithm (SGA) and proposes a new methodology to handle a complex variety of variables in a typical SC problem. To achieve this aim, three new genetic operators-knowledge based: initialization, selection, crossover, and mutation are introduced. The methodology developed here helps to improve the performance of classical GA by obtaining the results in fewer generations. To show the efficacy of the algorithm, KBGA also tested on the numerical example which is taken from the literature. It has also been tested on more complex problems.
KW - Genetic Algorithm
KW - Knowledge Based Genetic Algorithm
KW - Knowledge Management
KW - Supply chain
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U2 - 10.1016/j.dss.2011.10.024
DO - 10.1016/j.dss.2011.10.024
M3 - Article
AN - SCOPUS:82255193981
SN - 0167-9236
VL - 52
SP - 528
EP - 538
JO - Decision Support Systems
JF - Decision Support Systems
IS - 2
ER -