Increasing demand for energy is pushing decision-makers in the Bioethanol Supply Chain Network (BSCN) to adopt second-generation biomass feedstocks to meet sustainability criteria. This study proposes an integrated Machine Learning (ML) and quantitative optimization model to design a Sustainable...

Modeling tools and technologies that will allow reaching decarbonization goals in the most cost-effective way are imperative for the transition to a climate-friendly energy system. This includes models which are able to optimize the design of energy systems with a large number of spatially...

The research objective of the present study is the development of a model for increased accuracy of steel-price forecasts, which is of paramount importance for firms who use steel as an input and thus need to make informed decisions with regard to an optimal amount and type of hedge against...

Distributed renewable energy systems are now widely installed in many buildings, transforming the buildings into 'electricity prosumers'. Additionally, managing shared energy usage and trade in smart buildings continues to be a significant difficulty. The main goal of solving such problems is to...

Improving energy efficiency, production costs, greenhouse gas emissions, and reliability are some of the most important problems in the energy industry. Energy storage systems and demand-side management (DSM) programs are among the existing solutions for removing such problems. This study...

To complete a successful energy project, all aspects and criteria which can influence the project should be considered. Classic time-cost trade-off (TCT) is used when the completion time of project is not acceptable for customers or top managers. This paper proposes a new...