Collaborative Self-Configuration and Learning in Autonomic Computing Systems: Applications to Supply Chain

摘要

Efficient supply chains should be responsive to demand surges and supply disruptions resulting from internal and external vulnerabilities. Firms can respond to vulnerabilities by either, reallocating and redirecting existing capacity, or, maintaining redundant capacity. Responding to these disruptions depends on efficient real-time decision-making through information sharing and collaboration. The concept of Information Supply Chains captures this focus on information flows between the various entities in the supply chain. In this paper, we use autonomic principles of self-optimization and self-configuration to address demand surges in the context of healthcare information supply chains that have been disrupted by epidemics. We build a prototype system using a multi-agent systems platform and the Autonomic Computing Toolkit, to illustrate how autonomic computing approaches can facilitate resource allocation decisions in responding to public health emergencies.