Fuzzy genetic algorithm based model for bull whip effect reduction in a supply chain

Abstract

Managing a supply chain (SC) is very difficult and challenging, since various sources of uncertainty and complex interrelationships between each member exist in the SC. The bullwhip effect refers to the phenomenon where orders to the supplier tend to have larger variance than sales to the buyer (i.e. demand distortion), and the distortion propagates upstream in an amplified form (i.e. variance amplification). Allocating optimal ordering quantity and mitigation of bullwhip effect is one of the challenging sectors in a modern multi echelon supply chain system. This study considers a multistage supply chain, where each stage orders to its immediate upstream stage to fulfill demand from its immediate downstream stage. The study develops an interactive Fuzzy Based Genetic Algorithm (FBGA) approach for reducing bull whip effect through reducing total supply chain cost as well as to determine optimal ordering quantity in a multi-stage, multi-period supply chain using Fuzzy Logic combined with Genetic Algorithm. To face the real world uncertainty, forecasted customer demand and other supply chain cost related parameters are considered here as imprecise value which are modeled through triangular fuzzy membership function. The proposed approach attempts to minimize total supply chain costs with reference to inventory or backorder cost, ordering cost, distribution cost and storage and production capacity constraints for different members of a multi stage supply chain. This proposed approach uses the strategy of simultaneously minimizing the most possible value, the most pessimistic value & also most optimistic value of the imprecise total costs. Here the author employed different unique genetic algorithm parameters for solving this nondeterministic supply chain cost minimization problem. Moreover, the proposed model provides the decision maker (DM) with alternative decision plans for different degrees of satisfaction. In addition, the proposed model provides a systematic framework to facilitate decision making, enabling a DM to interactively modify the fuzzy data and parameters until a satisfactory solution is obtained. For reinforcing and accelerating the decision making for the decision maker a case study was considered at Nestle Bangladesh Limited. The proposed method is effective and easy to implement in practical management and supply chain systems. In nutshell a very recent & potential heuristic algorithm is developed and used in this study so that the researchers as well as the supply chain members can have the guideline to implement successful framework to reduce bull whip effect in a multistage supply chain system.