Tackling Industrial-Scale Supply Chain Problems by Mixed-Integer Programming (Journal of Computational Mathematics, 2021)
The modeling flexibility and the optimality guarantees provided by mixed-integer programming greatly aid the design of robust and future-proof decision support systems. Thecomplexity of industrial-scale supply chain optimization, however, often poses limits tothe application of general mixed-integer programming solvers. In this paper we describealgorithmic innovations that help to ensure that MIP solver performance matches the complexity of the large supply chain problems and tight time limits encountered in practice.Our computational evaluation is based on a diverse set, modeling real-world scenariossupplied by our industry partner SAP.