订单制造中基于agent的动态订单接受策略

Juan Hao, Jianjun Yu
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引用次数: 0

摘要

订单接受是订单制造企业成功的关键因素。在这项工作中,为了在无限规划范围内最大化平均收益,我们使用动态规划来建模订单接受问题,并使用强化学习方法来解决它。提出了一种利用平均奖励强化学习进行动态订单接受仿真开发的新方法。通过仿真,学习智能决策策略来动态控制即将到来的订单。与先到先得(FCFS)的比较突出了提出的新方法在最大化平均收入方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Agent-Based Dynamic Order Acceptance Policy in Make-to-Order Manufacturing
Order acceptance is a key success factor in make-to-order (MTO) manufacturing firms. In this work, in order to maximize average revenue in an infinite planning horizon, we use dynamic programming to model the order acceptance problem, and solve it with reinforcement learning approach. A novel approach for simulation-based development for dynamic order acceptance using average-reward reinforcement learning is proposed. Through the simulation, an intelligent decision policy to dynamically control the coming orders is learned by the agent. Comparisons made with First-Come-First-Serve (FCFS) highlight the effectiveness of the proposed novel approach to maximize the average revenue.
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