Predicting solutions of large-scale optimization problems via machine learning: A case study in blood supply chain management

• Published in: Computers & operations research Vol. 119; pp. 104941 - 20
• Main Authors: Abbasi, Babak, Babaei, Toktam, Hosseinifard, Zahra, Smith-Miles, Kate, Dehghani, Maryam
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01-07-2020; Pergamon Press Inc
• Subjects: Blood; Blood supply chain; CART; Data mining; Decisions; Hospitals; k-NN; Large-Scale optimization; Machine learning; Neural networks; Operations research; Optimization; Perishable inventory management; Research facilities; Solvers; Supply chain management; Supply chains; k-NN; Neural networks; Large-Scale optimization; Blood supply chain; Machine learning; Data mining; Perishable inventory management; CART
• DOI: 10.1016/j.cor.2020.104941
• EISSN: 1873-765X; 0305-0548
• ISSN: 0305-0548

•A new methodology is developed that uses machine learning to predict the solution of large-scale stochastics optimization models.•The proposed approach is discussed in the context of operational decision making and operations management.•The proposed approach is tested on the blood transhipment problem under demand uncertainty.•Various machine learning models including ANN, KNN, CART and RF are implemented and compared.
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.