* Corresponding author E-mail address: shahed_07ipe@yahoo.com (S. Mahmud) © 2016 Growing Science Ltd. All rights reserved. doi: 10.5267/j.uscm.2015.11.001 Uncertain Supply Chain Management 4 (2016) 137–146 Contents lists available at GrowingScience Uncertain Supply Chain Management homepage: www.GrowingScience.com/uscm Minimizing the bullwhip effect in a single product multistage supply chain using genetic algorithm Shahed Mahmud * , Md. Sohanur Rahman, Md. Mahamudul Hasan and Md. Mosharraf Hossain Department of Industrial & Production Engineering, Rajshahi University of Engineering & Technology Rajshahi-6204, Bangladesh C H R O N I C L E A B S T R A C T Article history: Received March 20, 2014 Received in revised format November 18, 2015 Accepted November 18 2015 Available online November 18 2015 Supply chain management is important for companies and organizations to improve their business and lead competitiveness in the global marketplace. But demand variations in the supply chain are significant problem for most practitioners, planners, demand managers, and operations managers. Demand variations make forecasting and inventory management more difficult and tend to increase inventory levels. The supply chain (SC) profitability can be affected by the cost associated with large inventories, transportation, and production due to the bullwhip effect. Only bullwhip effect can lead to reduce the supply chain profitability in great amount. This paper represents a computational intelligence approach, which addresses the bullwhip effect in multistage supply chain. As a computational intelligence approach, Genetic Algorithm (GA) is employed to reduce the bullwhip effect. Through this approach, optimal order quantity in each stage is to be calculated by considering cost associated with bullwhip effect. Distorted information from one end of a supply chain can lead to tremendous inefficiencies to other end. In this paper it is shown that if each player of the supply chain orders or transfers optimum quantities for the upcoming period then the bullwhip effect can be reduced significantly. Growing Science Ltd. All rights reserved. 6 © 201 Keywords: Profitability Bullwhip effect Genetic algorithm Optimal ordering quantity 1. Introduction Demand variability amplification across the supply chain, known as Bullwhip Effect (BWE), results in serious inefficiencies across the chain. When each player manages its own organization without any coordination to the others, it might cause “Bullwhip Effect” (BWE); variances of ordering patterns move up to the SC from end customer to retailer, to distributor, manufacturer, to Supplier. Lee et.al. (1997a) stated that Procter and Gamble were one of the first companies to identify the bullwhip effect through examining the order patterns for one of their products. Forrester (1958) first demonstrated the term bullwhip effect and motivated many researchers to work on this issue. Lee et al. (1997a) claimed that the merely demand fluctuation in retail shops lead to greater order variability due to information