This paper considers a distributed decision-making approach for manufacturing
task assignment and condition-based machine health maintenance. Our approach
considers information sharing between the task assignment and health management
decision-making agents. We propose the design of the decision-making agents
based on Markov decision processes. The key advantage of using a Markov
decision process-based approach is the incorporation of uncertainty involved in
the decision-making process. The paper provides detailed mathematical models
along with the associated practical execution strategy. In order to demonstrate
the effectiveness and practical applicability of our proposed approach, we have
included a detailed numerical case study that is based on open source milling
machine tool degradation data. Our case study indicates that the proposed
approach offers flexibility in terms of the selection of cost parameters and it
allows for offline computation and analysis of the decision-making policy.
These features create and opportunity for the future work on learning of the
cost parameters associated with our proposed model using artificial
intelligence.

本文提出了一种分布式决策方法，用于制造任务分配和基于条件的机器健康维护，并基于马尔可夫决策过程设计决策代理，以处理决策过程中涉及的不确定性。通过数值案例研究，证明该方法具有灵活性和实用性，并可以用人工智能学习成本参数。