Multi-agent learning mechanism design and simulation of multi-echelon supply chain

Abstract

A supply chain multi-agent learning mechanism based on the Particle Swarm Optimization algorithm is designed. The manufacturer’s profit and the product utility are taken as objective functions to explore the influences of social learning ability and self-learning ability on the manufacturer’s optimal price, optimal advertising investment, optimal profit, product utility, and supply chain competition. This research demonstrates the influences of different learning abilities on the evolution of the supply chain. The simulation results show that the most appropriate social learning ability can enhance competition between manufacturers and improve product utility. With the enhancement of self-learning ability, manufacturers have a wider range of pricing, products are more diverse, and consumers have a wider choice of goods. This study shows certain guiding significance for the scientific management of the supply chain and the optimization of the competitive strategy of the enterprises on the chain.