Network optimization in supply chain: A KBGA approach |
Abstract:
In this paper, we present a Knowledge Based Genetic Algorithm (KBGA) for the network optimization ofSupply 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 hasbeen 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 handlea 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 developedhere 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 theliterature. It has also been tested on more complex problems. |
| Keywords: |
Supply chain Knowledge Management Genetic Algorithm Knowledge Based Genetic Algorithm |
| Author(s): |
A. Prakash, Felix T.S. Chan , H. Liao , S.G. Deshmukh |
| Source: |
Decision Support Systems 52 (2012) 528–538 |
| Subject: |
مدیریت دانش |
| Category: |
مقاله مجله |
| Release Date: |
2012 |
| No of Pages: |
11 |
| Price(Tomans): |
0 |
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