用强化学习优化双源供应，降低关键部件故障风险
Solving Dual Sourcing Problems with Supply Mode Dependent Failure Rates
- 提出结合强化学习与内生参数化学习的新算法
- 案例中平均成本降低22.6%，91.1%场景优于基线
- 可统一处理多种零件和参数，适合工业备件管理
本文研究了供应模式依赖失效率的双源供应问题，尤其适用于停机关键资产的备件管理。为提升韧性，企业越来越多地采用传统制造与增材制造相结合的双源策略。然而，不同制造方式生产的零件具有不同的失效特性，影响未来需求。为此，本文提出一种新的迭代启发式算法及多种强化学习方法，结合内生参数化学习（EPL）框架。该框架可兼容任意学习方法，使单一策略能处理多类零件和多种输入参数。在简化场景中，最优策略平均优化差距仅为0.4%；在能源行业案例研究中，政策在91.1%的实例中优于基线，平均成本降低最高达22.6%。
原文摘要 · Abstract (English)
This paper investigates dual sourcing problems with supply mode dependent failure rates, particularly relevant in managing spare parts for downtime-critical assets. To enhance resilience, businesses increasingly adopt dual sourcing strategies using both conventional and additive manufacturing techniques. This paper explores how these strategies can optimise sourcing by addressing variations in part properties and failure rates. A significant challenge is the distinct failure characteristics of parts produced by these methods, which influence future demand. To tackle this, we propose a new iterative heuristic and several reinforcement learning techniques combined with an endogenous parameterised learning (EPL) approach. This EPL approach - compatible with any learning method - allows a single policy to handle various input parameters for multiple items. In a stylised setting, our best policy achieves an average optimality gap of 0.4%. In a case study within the energy sector, our policies outperform the baseline in 91.1% of instances, yielding average cost savings up to 22.6%.
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