基于平均强化学习的订单生产方式企业订单接受策略

从收益管理思想出发，采用平均强化学习算法研究不确定环境下订单生产(MTO)方式企业的订单接受问题。以最大化平均期望收益为优化目标，采用多级价格机制，把订单类型、价格和提前期的不同组合作为系统状态划分标准，结合平均强化学习原理，提出了具有学习能力的订单接受算法(RLOA)。仿真结果表明，RLOA算法具有学习和选择性接受订单的能力，与其他订单接受规则相比，在平均收益、订单类型接受状况和适应性等方面都有较好表现。

Order acceptance policy in Make-to-Order manufacturing based on average-reward reinforcement learning

From the perspective of revenue management, a new approach for order acceptance under uncertainty in Make-to-Oder (MTO) manufacturing using average-reward reinforcement learning was proposed. In order to maximize the average expected revenue, the proposed approach took order types and different combinations of price and leadtime as criteria for the classification of the system states based on multi-level pricing mechanism. The simulation results show that the proposed algorithm has learning and selective ability to accept the order. Comparisons made with other order acceptance policies show the effectiveness of the proposed algorithm in average revenue, accepted order types, and adaptability.