Hybrid combinatorial remanufacturing strategy for medical equipment in the pandemic.
Hybrid combinatorial remanufacturing strategy for medical equipment in the pandemic.
复制标题
疫情下医疗设备混合组合再制造策略
DOI：
10.1016/j.cie.2022.108811
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发表时间：
2022-12
影响因子：
7.9
通讯作者：
Li, Sijie
中科院分区：
文献类型：
--
作者：
Shang, You;Li, Sijie
关键词：
新冠肺炎疫情冲击了医疗供应链，造成医疗设备严重短缺。为了满足迫切的需求，一个现实的方法是收集废弃的医疗设备，然后再制造，其中拆卸的模块与所有库存单位（sku）共享，以提高利用率。然而，在紧急情况下，设备应该被顺序地立即处理，这意味着在有限的信息下做出的决定是短视的。提出了一种混合组合再制造（HCR）策略，并开发了基于Q学习和双深度Q网络的强化学习框架来寻找最优修复方案。在该框架中，我们将HCR问题转化为一个迷宫探索游戏，提出了重加权有效动作的递减贪心选择规则（DeSoRVA）和Espertate知识字典，将成本最小化目标与人类判断和问题的全局状态相结合。进一步实现了在运设备质量状态未知的实时环境。数值研究表明，我们的算法可以学习节省成本，并且问题规模越大，成本越低。此外，esperate提炼的复杂知识是有效和稳健的，可以处理与大流行的波动性相对应的不同规模的再制造问题。
The COVID-19 pandemic hit the medical supply chain, creating a serious shortage of medical equipment. To meet the urgent demand, one realistic way is to collect abandoned medical equipment and then remanufacture, where the disassembled modules are shared with all stock-keeping units (SKUs) to improve utilization. However, in an emergency, the equipment should be processed sequentially and immediately, which means the decision is short-sighted with limited information. We propose a hybrid combinatorial remanufacturing (HCR) strategy and develop two reinforcement learning frameworks based on Q-learning and double deep Q network to find the optimal recovery option. In the frameworks, we transform HCR problem into a maze exploration game and propose a rule of descending epsilon-greedy selection on reweighted valid actions (DeSoRVA) and Espertate knowledge dictionary to combine the cost-minimizing objective with human judgment and the global state of the problem. A real-time environment is further implemented where the quality status of the in-transit equipment is unknown. Numerical studies show that our algorithms can learn to save cost, and the larger scale of the problem is, the more cost-down can be achieved. Moreover, the sophisticated knowledge refined by Espertate is effective and robust, which can handle remanufacturing problems at different scales corresponding to the volatility of the pandemic.
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