Fuzzy genetic algorithm-based model for bullwhip effect reduction in a multi-stage supply chain

Abstract

Synchronisation among supply chain (SC) stages is essential, but difficult to achieve, which results in a bullwhip effect. This paper proposes an interactive fuzzy-based genetic algorithm approach for reducing bullwhip effect by minimising total SC cost as well as to determine optimal ordering quantities in a multi-stage, multi-period SC using fuzzy logic combined with genetic algorithm. To face the uncertainty, forecasted customer demand and other SC cost related parameters are considered as uncertain parameters which are modelled through triangular fuzzy membership function. We used the strategy of simultaneously minimising the most possible value, the most pessimistic value and the most optimistic value of the total costs. Finally, a real-life case study is solved with the help of Matlab software to illustrate the usefulness of the approach where we employed different unique genetic algorithm parameters and compared the result obtained with existing policy and using only genetic algorithm.