Abstract Practical constrained optimization models are often large, and solving them in a reasonable time is a challenge in many applications. Further, many industries have limited access to professional commercial optimization solvers or computational power for use in their day-to-day operational decisions. In this paper, we propose a novel approach to deal with the issue of solving large operational stochastic optimization problems (SOPs) by using machine learning models. We assume that decision makers have access to facilities to optimally solve their large-scale optimization model for some initial and limited period and for some test instances. This might be through a collaborative project with research institutes or through short-term use of high-performance computing facilities. We propose that longer term support can be provided by utilizing the solutions (i.e., the optimal value of the actionable decision variables) of the stochastic optimization model from this initial period to train a machine learning model to learn optimal operational decisions in the future. In this study, the proposed approach is employed to make decisions on transshipment of blood units in a network of hospitals. We compare the decisions learned by several machine learning models with the optimal results obtained if the hospitals had access to commercial optimization solvers and computational power, and with the hospital network’s current empirical heuristic policy. The results show that using a trained neural network model reduces the average daily cost by about 29% compared with current policy, while the exact optimal policy reduces the average daily cost by 37%. Although optimization models cannot be fully replaced by machine learning, our proposed approach while not guaranteed to be optimal can improve operational decisions when optimization models are computationally expensive and infeasible for daily operational decisions in organizations such as not-for-profit and small and medium-sized enterprises.

中文翻译：

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通过机器学习预测大规模优化问题的解决方案：血液供应链管理案例研究

摘要 实际的约束优化模型通常很大，在合理的时间内求解它们在许多应用中是一个挑战。此外，许多行业在日常运营决策中使用专业商业优化求解器或计算能力的机会有限。在本文中，我们提出了一种新方法来解决使用机器学习模型解决大型操作随机优化问题 (SOP) 的问题。我们假设决策者可以使用设施来优化解决某些初始和有限时期以及某些测试实例的大规模优化模型。这可能是通过与研究机构的合作项目或通过短期使用高性能计算设施来实现的。我们建议通过利用初始阶段的随机优化模型的解决方案（即可操作决策变量的最佳值）来训练机器学习模型以学习未来的最佳操作决策，可以提供更长期的支持。在这项研究中，所提出的方法被用来对医院网络中的血液单位转运做出决定。我们将几种机器学习模型学习到的决策与医院获得商业优化求解器和计算能力时获得的最佳结果，以及医院网络当前的经验启发式策略进行比较。结果表明，与当前策略相比，使用经过训练的神经网络模型可以将平均每日成本降低约 29%，而精确最优策略将平均每日成本降低了 37%。虽然优化模型不能完全被机器学习取代，但我们提出的方法虽然不能保证是最优的，但当优化模型的计算成本高昂且对于非营利组织和中小型组织等组织的日常运营决策不可行时，可以改善运营决策。规模的企业。