Maximisation of total supply chain profit and minimisation of bullwhip effect in a multi-echelon supply chain network using particle swarm optimisation and genetic algorithm

The presence of numerous sources, nature of uncertainties and complex interrelationships among various entities make supply chain a complex network. Bullwhip effect is one of the major issues of complexity. This research intended to maximise profit as well as to reduce bullwhip effect in a supply chain by finding optimal forecasted sales quantities for each entity. A multi-product, multi-stage and multi-period supply chain model was developed considering previous market demands where the objective function was developed to maximise total supply chain profit. Later, the proposed model was applied to analyse a realistic case of supply chain network. Simulations were conducted using genetic algorithm and particle swarm optimisation to find optimal forecasted sales quantities and were then compared. Results revealed that total profit of the entire supply chain network significantly increased and the resulting optimal forecasted sales quantities reduced order variability, which consequently minimised the bullwhip effect in supply chain network.