Estimation of Weighting Factors for Multi-Objective Scheduling Problems using Input-Output Data

Abstract

for other one is the estimation method based on inverse optimization. These methods are applied to three-objectives parallel machine scheduling problems, whose objective functions consist of makespan, the weighted sum of completion time, the weighted sum of tardiness, the weighted sum of earliness and tardiness, and setup costs. The accuracy of the proposed machine learning and inverse optimization methods is evaluated. A surrogate model that learns input-output data is proposed to reduce the computational efforts. Computational results show the effectiveness of the proposed method for weighting factors in the objective function from the optimal solutions.