Supervised Learning of the Optimal Objective Function Value in Chemical Production Scheduling

Mixed-integer programming (MIP) can be used to formulate and solve complex production scheduling problems in the field of process systems engineering. However, the solution of MIP models may require a long computing time due to the combinatorial complexity of the problems. In this work, we propose s...

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Bibliographic Details
Main Author:	Teemu J. Ikonen (20724395) (author)
Other Authors:	Boeun Kim (1234278) (author), Christos T. Maravelias (206037) (author), Iiro Harjunkoski (2331031) (author)
Published:	2025
Subjects:	Biochemistry Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified studied objective functions process systems engineering predictions allow us different problem features 2 </ sup best prediction models prediction models r </ >< sup supervised learning makespan minimization integer programming instance parameters high  four classes combinatorial complexity
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Supervised Learning of the Optimal Objective Function Value in Chemical Production Scheduling

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