Machine Learning and Inverse Optimization for Estimation of Weighting Factors in Multi-Objective Production Scheduling Problems

In recent years, scheduling optimization has been utilized in production systems. To construct a suitable mathematical model of problem, modeling techniques that can automatically select an appropriate objective function from historical data are necessary. This paper presents two methods to estimate weighting factors the problem data, given information operation time and setup costs. We propose machine learning-based method, inverse optimization-based method using input/output problems when unknown. These applied multi-objective parallel real-world chemical batch plant problem. The results estimation accuracy evaluation show proposed for estimating effective.