Employing Genetic Algorithms to minimise the Bullwhip Effect in an online efficient-responsive supply chain

The bullwhip effect in supply chains has been observed through a number of previous important works. How to effectively minimise the bullwhip effect, however, remains under-investigated, and is still an open research topic. This paper investigates whether genetic algorithms (GAs) can effectively minimise the bullwhip effect in an efficient-responsive supply chain. To achieve this goal, we established a comprehensive model for such a supply chain with orders updated on a weekly basis, and then the GAs were utilised to find the optimal ordering policy, and lead time sets for supply chain participants employing a moving average forecasting technique. An important contribution of this research is that the simulated supply chain is online and efficient-responsive, and hence more realistic than existing models. Experimental results demonstrate that the genetic algorithm is effective in minimising the bullwhip effect.