Learning by Objectives for Adaptive Shop‐Floor Scheduling*
提出一种利用遗传搜索的自动学习方案，用于分散式车间调度中的自适应控制，通过多智能体框架实现不同工作站根据目标进行调度决策，并仿真分析不同目标对系统性能的影响。
ABSTRACT Effective production scheduling requires consideration of the dynamics and unpredictability of the manufacturing environment. An automated learning scheme, utilizing genetic search, is proposed for adaptive control in typical decentralized factory‐floor decision making. A high‐level knowledge representation for modeling production environments is developed, with facilities for genetic learning within this scheme. A multiagent framework is used, with individual agents being responsible for the dispatch decision making at different workstations. Learning is with respect to stated objectives, and given the diversity of scheduling goals, the efficacy of the designed learning scheme is judged through its response under different objectives. The behavior of the genetic learning scheme is analyzed and simulation studies help compare how learning under different objectives impacts certain aggregate measures of system performance.