Supply Chain Strategies, Issues and Models by Usha Ramanathan & Ramakrishnan Ramanathan

Author:Usha Ramanathan & Ramakrishnan Ramanathan
Language: eng
Format: epub
Publisher: Springer London, London

Hybrid Genetic Algorithm 1 (HGA1)

Hybrid Genetic Algorithm 1 (HGA1) is an adapted version of the HGA proposed by Sethupathi and Rajendran (2010). In their work, Sethupathi and Rajendran employed a crossover operator using a combination of the arithmetic crossover operator and the gene-wise crossover operator to obtain the best heuristic order-up-to levels and review periods at installations. In the present work, we adapt their HGA to determine the best base-stocks and re-order points at installations. A chromosome is first obtained from the entire population by using the arithmetic crossover operator as follows. Assume that five chromosomes C1 to C5 are present in par_pop as shown in Fig. 5. Let the relative fitness values of these chromosomes be 0.30, 0.25, 0.20, 0.15, and 0.10. We make use of these values to construct the arithmetic offspring. The first gene in the offspring is constructed by an arithmetic operation (i.e., the first gene value of the first chromosome multiplied by its relative fitness plus the first gene value of the second chromosome multiplied by its relative fitness, and so on to obtain the first gene value of the offspring). For example, the value of first gene in the offspring is as follows:

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