Behaviour adaptation in the multi-agent, multi-objective and multi-role supply chain

Researchers, practitioners and enterprise software providers are realising the potential of agent-based technology to automate supply chain procurement to achieve consistent, traceable decision making. As the complexity of supply chains grow, these systems will gain more attention. In this paper, we model and simulate a complex autonomous supply chain managed by computational agents that aim to minimise lead time and maximise revenue through evolutionary multi-objective optimisation. The agents are in a competitive environment where they take on the roles of both client and producer. In addition to optimising their production strategy, they also have the opportunity to dynamically fine-tune their decision parameters when it comes to selecting their own suppliers, using the Analytical Hierarchy Process. It is observed that computational agents are capable of functioning in such complex environments, effectively converging to policies in synergy with their market. Multi-objective, multi-role optimisation results in better overall supply chain performance than tests where agents have single-objectives and single-roles. Our study forms an exploratory step towards more realistic agent-based supply chains where analytical methods are unavailable.