This paper proposes an integrated optimization algorithm (IOA) designed for solving complicated optimization problems that are non-convex, non-differentiable, non-continuous, or computationally intensive. IOA is synthesized from 5 sub-algorithms:

In traditional machine learning techniques, the degree of closeness between true and predicted values generally measures the quality of predictions. However, these learning algorithms do not consider prescription problems where the predicted values will

The economic concern, environmental awareness related legislations, and potential recycling benefits are the main motives that have changed the dimension of supply chain network with environmentally effective transportation and efficient scrap collection

Many real-world optimization problems involve uncertain parameters with probability distributions that can be estimated using contextual feature information. In contrast to the standard approach of first estimating the distribution of uncertain parameters

The optimal co-planning of the integrated energy system (IES) and machine learning (ML) application on the multivariable prediction of IES parameters have mostly been carried out separately in the literature. Meanwhile, the synergy of optimization