A New Parallel Genetic Algorithm for Reducing the Bullwhip Effect in an Automotive Supply Chain

Abstract With the developments in the automotive industry, most of the car manufacturing companies aim to move from stock oriented production of cars through a built order production. There have been many improvements in recent years in terms of delivery times, reliability and responsiveness. Shortening the delivery times, increasing the reliability and the responsiveness perform the most of the efforts in car manufacturing optimization. Foreseen vulnerability of bullwhip effect for short-term and mid-term car manufacturing and supply chain planning are considered. The bullwhip effect occurring with respect to demand order fluctuations is transferred while moving up the supply chain. Information deterioration end to end in a supply chain causes drastic ineffectiveness and inefficiencies. Companies should understand the causes to overcome extra costs over the supply chain. In this paper, a supply chain model based on the MIT Beer Game for automotive industry is discussed. Finally, a parallel genetic algorithm to reduce the bullwhip effect and cost in an automotive supply chain is proposed.

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