Journal article

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

B Abbasi, T Babaei, Z Hosseinifard, K Smith-Miles, M Dehghani

Computers & Operations Research | Elsevier | Published : 2020


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 r..

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